{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "tutorial-realtor",
   "metadata": {},
   "source": [
    "## 1 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fifteen-fiber",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-23T07:31:09.527191500Z",
     "start_time": "2024-06-23T07:31:07.686389700Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "from sklearn.metrics import roc_auc_score,roc_curve,auc  \n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn.linear_model import LogisticRegression   \n",
    "import numpy as np  \n",
    "import math  \n",
    "import xgboost as xgb  \n",
    "import toad  \n",
    "# 加载数据\n",
    "data_all = pd.read_csv(\"scorecard.txt\")\n",
    "\n",
    "# 指定不参与训练列名  \n",
    "ex_lis = ['uid', 'samp_type', 'bad_ind']  \n",
    "# 参与训练列名  \n",
    "ft_lis = list(data_all.columns)  \n",
    "for i in ex_lis:      \n",
    "    ft_lis.remove(i) \n",
    "\n",
    "# 开发样本、验证样本与时间外样本  \n",
    "dev = data_all[(data_all['samp_type'] == 'dev')]\n",
    "val = data_all[(data_all['samp_type'] == 'val') ]  \n",
    "off = data_all[(data_all['samp_type'] == 'off') ]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "israeli-tribe",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>uid</th>\n",
       "      <th>td_score</th>\n",
       "      <th>jxl_score</th>\n",
       "      <th>mj_score</th>\n",
       "      <th>rh_score</th>\n",
       "      <th>zzc_score</th>\n",
       "      <th>zcx_score</th>\n",
       "      <th>person_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
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       "      <th>samp_type</th>\n",
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       "       bad_ind                     uid  td_score  jxl_score  mj_score  \\\n",
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       "1          0.0                A1000011  0.315406   0.629745  0.535854   \n",
       "2          0.0               A10000481  0.002386   0.609360  0.366081   \n",
       "3          0.0                A1000069  0.406310   0.405352  0.783015   \n",
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       "...        ...                     ...       ...        ...       ...   \n",
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       "\n",
       "       rh_score  zzc_score  zcx_score  person_info  finance_info  credit_info  \\\n",
       "0      0.843725   0.605194   0.406122    -0.128677      0.023810         0.00   \n",
       "1      0.197392   0.614416   0.320731     0.062660      0.023810         0.10   \n",
       "2      0.342243   0.870006   0.288692     0.078853      0.071429         0.05   \n",
       "3      0.563953   0.715454   0.512554    -0.261014      0.023810         0.00   \n",
       "4      0.129552   0.544368   0.137676     0.013863      0.023810         0.50   \n",
       "...         ...        ...        ...          ...           ...          ...   \n",
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       "\n",
       "       act_info samp_type  \n",
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       "1      0.448718       dev  \n",
       "2      0.179487       dev  \n",
       "3      0.423077       dev  \n",
       "4      0.166667       dev  \n",
       "...         ...       ...  \n",
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       "95802  0.076923       off  \n",
       "95803  0.076923       off  \n",
       "95804  0.076923       off  \n",
       "95805  0.076923       off  \n",
       "\n",
       "[95806 rows x 13 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "mineral-africa",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.322581</td>\n",
       "      <td>-0.053718</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>finance_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>35</td>\n",
       "      <td>0.036763</td>\n",
       "      <td>0.039687</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>1.02381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>100</td>\n",
       "      <td>0.063626</td>\n",
       "      <td>0.143098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>act_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>74</td>\n",
       "      <td>0.236197</td>\n",
       "      <td>0.157132</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.089744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samp_type</th>\n",
       "      <td>object</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>dev:68.16%</td>\n",
       "      <td>off:16.67%</td>\n",
       "      <td>val:15.16%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dev:68.16%</td>\n",
       "      <td>off:16.67%</td>\n",
       "      <td>val:15.16%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 type   size missing  unique     mean_or_top1     std_or_top2  \\\n",
       "bad_ind       float64  95806   0.00%       2         0.018767        0.135702   \n",
       "uid            object  95806   0.00%   95806  A11130039:0.00%  A2043929:0.00%   \n",
       "td_score      float64  95806   0.00%   95806         0.499739        0.288349   \n",
       "jxl_score     float64  95806   0.00%   95806         0.499338         0.28885   \n",
       "mj_score      float64  95806   0.00%   95806          0.50164        0.288679   \n",
       "rh_score      float64  95806   0.00%   95806         0.498407        0.287797   \n",
       "zzc_score     float64  95806   0.00%   95806         0.500627        0.289067   \n",
       "zcx_score     float64  95806   0.00%   95806         0.499672        0.289137   \n",
       "person_info   float64  95806   0.00%       7        -0.078229        0.156859   \n",
       "finance_info  float64  95806   0.00%      35         0.036763        0.039687   \n",
       "credit_info   float64  95806   0.00%     100         0.063626        0.143098   \n",
       "act_info      float64  95806   0.00%      74         0.236197        0.157132   \n",
       "samp_type      object  95806   0.00%       3       dev:68.16%      off:16.67%   \n",
       "\n",
       "                  min_or_top3      1%_or_top4     10%_or_top5  \\\n",
       "bad_ind                   0.0             0.0             0.0   \n",
       "uid           A14545729:0.00%  A2853508:0.00%  A7182876:0.00%   \n",
       "td_score             0.000005        0.009613        0.099706   \n",
       "jxl_score            0.000013        0.009947        0.099103   \n",
       "mj_score             0.000007        0.010508        0.100882   \n",
       "rh_score             0.000005        0.009916        0.099948   \n",
       "zzc_score            0.000012        0.010186        0.099011   \n",
       "zcx_score             0.00001        0.010325        0.099743   \n",
       "person_info         -0.322581       -0.322581       -0.322581   \n",
       "finance_info          0.02381         0.02381         0.02381   \n",
       "credit_info               0.0             0.0             0.0   \n",
       "act_info             0.076923        0.076923        0.076923   \n",
       "samp_type          val:15.16%            None            None   \n",
       "\n",
       "               50%_or_bottom5 75%_or_bottom4  90%_or_bottom3   99%_or_bottom2  \\\n",
       "bad_ind                   0.0            0.0             0.0              1.0   \n",
       "uid           A10838556:0.00%  A459089:0.00%  A1083355:0.00%  A11839218:0.00%   \n",
       "td_score             0.500719       0.747984        0.900024         0.990041   \n",
       "jxl_score            0.499795       0.748646        0.899703         0.989348   \n",
       "mj_score             0.503048       0.752032        0.899308         0.990047   \n",
       "rh_score             0.497466       0.747188        0.899286         0.989473   \n",
       "zzc_score            0.501688       0.750986        0.899924         0.990043   \n",
       "zcx_score             0.49913       0.750683        0.901942         0.989712   \n",
       "person_info         -0.053718       0.078853        0.078853         0.078853   \n",
       "finance_info          0.02381        0.02381        0.071429         0.214286   \n",
       "credit_info               0.0           0.06            0.18              0.8   \n",
       "act_info             0.205128       0.346154        0.487179         0.615385   \n",
       "samp_type                None           None      dev:68.16%       off:16.67%   \n",
       "\n",
       "              max_or_bottom1  \n",
       "bad_ind                  1.0  \n",
       "uid           A9127893:0.00%  \n",
       "td_score            0.999999  \n",
       "jxl_score           0.999985  \n",
       "mj_score            0.999993  \n",
       "rh_score            0.999986  \n",
       "zzc_score           0.999998  \n",
       "zcx_score           0.999987  \n",
       "person_info         0.078853  \n",
       "finance_info         1.02381  \n",
       "credit_info              1.0  \n",
       "act_info            1.089744  \n",
       "samp_type         val:15.16%  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toad.detector.detect(data_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desperate-alert",
   "metadata": {},
   "source": [
    "## 2 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "imposed-instruction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keep: 12 drop empty: 0 drop iv: 1 drop corr: 0\n"
     ]
    }
   ],
   "source": [
    "dev_slct1, drop_lst= toad.selection.select(dev, dev['bad_ind'], \n",
    "                                                   empty=0.7, iv=0.03, \n",
    "                                                   corr=0.7, \n",
    "                                                   return_drop=True, \n",
    "                                                   exclude=ex_lis) \n",
    "print(\"keep:\", dev_slct1.shape[1],  \n",
    "      \"drop empty:\", len(drop_lst['empty']), \n",
    "      \"drop iv:\", len(drop_lst['iv']),  \n",
    "      \"drop corr:\", len(drop_lst['corr']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-talent",
   "metadata": {},
   "source": [
    "## 3 卡方分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coated-steering",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'td_score': [0.7989831262724624], 'jxl_score': [0.4197048501965005], 'mj_score': [0.3615303943747963], 'zzc_score': [0.4469861520889339], 'zcx_score': [0.7007847486465795], 'person_info': [-0.2610139784946237, -0.1286774193548387, -0.0537175627240143, 0.013863440860215, 0.0626602150537634, 0.078853046594982], 'finance_info': [0.0476190476190476], 'credit_info': [0.02, 0.04, 0.11], 'act_info': [0.1153846153846154, 0.141025641025641, 0.1666666666666666, 0.2051282051282051, 0.2692307692307692, 0.358974358974359, 0.3974358974358974, 0.5256410256410257]}\n"
     ]
    }
   ],
   "source": [
    "# 得到切分节点  \n",
    "combiner = toad.transform.Combiner()  \n",
    "combiner.fit(dev_slct1, dev_slct1['bad_ind'], method='chi',\n",
    "                min_samples=0.05, exclude=ex_lis)  \n",
    "# 导出箱的节点  \n",
    "bins = combiner.export()  \n",
    "print(bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-annotation",
   "metadata": {},
   "source": [
    "## 4 Bivar图,调整分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "accomplished-hollow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='act_info', ylabel='prop'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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58z9QEiAVwpl/D85kNlsuqc2TTz7D55+vJSMjnfnz3yrZIEVERMoQJQKlrDjDGwBycnKYOvUJxo4dX2T7b7/9ip+fP//61zjuvnsAM2e+RG5uTqnFL2KUyMgqnDhxvPD98eMpBAUF4+fnV6w28fFbOH48BQB/f3+6du3G77/vLr0LEBER8TBKBErZhYYunOnFF/9DbOzt1KvXoMh2my2Lli2vZ9q053nzzYUkJR3jjTdml0rs7vTtt5sYNmwggwbdzpQpk8jKyix2m8zMTKZMeYShQ/tz11138t5780s5eikNrVu34ddfd5KYeBCATz/9mPbtOxa7zbp1a3jnnbm4XC7y8vJYt24N119/Q+lehIiIiAdRIlDKijN0YenSD7FYrPTqFXtWu+jojjzxxDMEBATi4+PD0KEj+Prr9W6LtzSoGowUR6VKYTz22JNMmTKJIUP6sW/fHzz44EPs3r2L4cMHX7ANwIMPjicrK5O77x7AffcNpVGjJtx55yADr0hERMRYmixcyiIjq7Br187C9+ca3rBy5Wfk5OQwfPhg8vPt5ObmMnz4YGbMeJXdu38jMDCQa69t+VdrF1Zr2f5jPNdwqeHDBzFx4iRMJtNF24wb9zAOhwNQNZjyrm3baNq2jS6yLTg4hPnzP7hgG4CgoCCefvo5t8coIiJSVpTtO8gyqHXrNsya9QqJiQepWTPqnMMb3nxzYeHPR48e4e67BxTe6KSkbGTBgreYNWsuVqsXixe/T+fOt5TqNZQ0VYMRERERKX0aGlTKijO84UJiY2/n2muvZ8SIuxgypB9+fv7cc88/SiFy91E1GBEREZHSpx4BAxRneMNpVatWY82abwrfm81mxowZV7gwUnlQnOFSF2oTH7+FevXqU7lyeGE1mA0b1pXqNYiIiIiUNeoREMOpGoyIiIhI6TO5/l7AvoxITc0iP//cw0Wk7NmyZRNvvDGb/Hw71avXYMqUpzly5DDTp08r7Ck5V5vg4BAyMjKYMeNZ9u37E5PJRPv2nbj33pGYzcpzKyL7n3vJ/vYbAnr1wVIpzOhwRESkjLJazVSqFGB0GG6lREBEygWX00n2hrXYvvwcXC4sVasTOmY8Jh8fo0MTEZEyqCIkAnpkKiJlntOWRfr8N7Gt/Azvq68jaOgIHMeOkLFoIS6nHhiIiIiciyYLi0iZZk88SMa7b+NMP0VAn3743tQBk8mEs/cpspZ/jG3VCgJ69DY6TBEREY+jROAShFTyx9tquXjDMiov38GpVJvRYYgUi8vlImfLJrKWL8UcFETI6IfwiqpduN83uiP5SUfJXrcaS2QVfFu2Mi5YERERD6RE4BJ4Wy28/PVvRofhNuM7NDE6BJFiceXmkvnxYnK3f49X46YEDbwbc0DRcZwmk4nAPnfiSEkm88MPsFxVGa9adQyKWERExPNojoB4PKfNRt7uX8latQLbxq9w5ecbHZIYKD/pKGkzZ5D70w/4d+9F8D0jz0oCTjNZrQTffR/m4FDS57+JI/VkKUcrIiLiudQjIB7F5XTiSEkm/0AC9gMJ5O9PwJF8rGCnyQQuF7k/bCNo4F1Yq9UwNlgpdTnbvyfzw0WYfHwIvn8M3vUbXfQYc0AAwSPu59Ss/5I+/01CxzyEyVuVhERERJQIiKGcOTnkJx74/xv/A/txZRfMUzD5+2OtVQefljdgrVUHr5q1yPtjD5kfLSLt1Rfx79oDv863YLKU33kbUsCVbydr+VJytmzCWqcuQUNGYAkJKfbx1siqBA25h/R33iBj0UKCht6LSetMiIhIBadEwAAHdnxP/NJ3ceTbuapGbToNexBvP/9itcm1ZbFxwSxSjx0Gl5OGbTtzXY/bDbqSS+NyuXCeOI79wH7yD+zDfmA/jqOH4a+lLCyRVfFucQ1etetirVUbS+WIs27WfJq1wKt2XTI/jcO2egV5u34hcOBdWCOrGnFJUgocJ0+Q8e475B86iF/HLvj36H1ZyZ9346YE9O5L1vKl2FZ/QUD3Xm6IVkREpOxQIlDKsjNOsX7+/+gz6TlCI6vx3UcL+G7pQjoMeaBYbbYt+4CASldx66hJ2HNzWPLUWKo2bEqVeo0NvKpzc9nzyE88WOTG35WZAYDJxxdrVC28u3QruPGPqoX5b8nQ+ZgDAggecg+5za8l85M40l55Af9uMfh16KynvOVM3q6dZCx+F1wugobdh0/za67ofL7Rncg/dpTsr1YVVBK67oYSilRERKTsUSJQyhJ//YmI2vUJjawGQNNO3flo6njaDx6JyWS6aJt2A+8rXCDJdioVZ34+3n6eseqdIy21YIjP/gTyDySQfzgR/orVXDkc70ZNCob41KqDpUrVK75p97nmOrzq1iNz6RJsK5aRt3MHQQPuwhIeURKXIwZyORzYVq0ge/0aLNVrEHzXCCyVw6/4vCaTicC+/XEcTyEz7v2CSkJnlBwVERGpSJQIlLKs1OMEVqpc+D6wUmXysm3Yc7ILhwddrI3JYuGrt15m3w/fUue6NoRWqVbq1+HKzyf/yKEik3qdp9IKdnp5Ya1ZC7+OXf668a+NOTDILXGYg4IJuvs+crd/T9anH5L68nQCet5WsKiUegfKJGd6OunvzyN/3x/43tiOgNg7MHl5ldj5CyoJ3UvazJdInzeX0HH/whJaqcTOLyIiUlYoEShlp5/m/92ZN63FadPlvvF0uOsBVr3+PD98Fker2EElG+jfODPSCyfz2g/sIz8xEfLtAJhDK2GtUxevWnWw1qqLtVr1Up3AazKZ8G3ZCq96Dcj8aBFZyz4mb+cOAvsPxhJW+eInEI9h/3Mv6e/Px5WTTeCAu/C94Ua3fI45IPD/KwnNm6tKQiIiUiEpEShlgVeFk5ywt/B9VtoJfPwD8fLxLVabxJ3bCatRi4DQMLx8/ajfuj0JP24p0RhdDgeOY0cLbvgP7Me+PwHnyeMFOy0WrNVr4ts2Gq/adbDWqoMlJLREP/9yWUJCCR7xALnbviNr+VLS/jsd/5g++LZpVzjsSjyTy+kke8NabF9+jqVyOEH3j8Hq5p6ugkpCw0l/Zw4Zi94laOgI9SKJiEiFokSglNVsei1b4uaRlnSE0Mhq7Nq4itrXti52mz+/38S+7VvocNconPn5/Pn9Zmo0vfaKYvLKzSYk+QjJH+8i/bffyU88gCs3FwBTUHDBk/7TN/7Va5boMI2SZjKZ8G3dFq8GjciMe5+spUvI2/kzgXcO1vAPD+W02chY/C7233bifc11BPYbjNnX9+IHlgDvxs0I6NWHrM8+USUhERG5InFxcSxcuJCAgABmzJhBzZo1C/edPHmSCRMmkJKSQs+ePRkzZgypqalMmzaNPXv24HA4mDp1KjfccAM5OTk88sgj/Pnnn7Ru3ZopU6ZgcdNIC5PL9VftxjImNTWL/PxzD6Fxl/DwIF7++rcrPs+BX75n69L3cOTnExxehc73jiM9JYmNC2Zx51OvnLeNb0AQubZMvn7vDU4ePojJBLWvvZFWtw0q/pNMl4uAtBOEphymUtJhQpMPE3jqr9VWzWYsVavjVas21tp1C8b2V7qqzD5Ndzmd5Hy3iazPl2GyWAi47XZ8brixzF5PeWRPPEjGu2/jTD9FQO++BXM7SvnPx+VykfnRInK3biFw8DBVEhIREQCsVjOVKhWvIMuJEycYMGAAy5cvZ9u2bcTFxTF79uzC/dOmTaN27doMGjSIQYMGMXXqVGrUqEFCQgItWrRg3bp1zJ8/n4ULFzJ//nySkpKYNGkS48aNo3fv3nTt2tUt16hE4BKUVCJQmiz2XEKTjxKacpjQpMOEphzBK6/gaX+ejy9pEdULX0P7dOJEep7BEZc8x/EUMuLeJz/hT7ybNifwjoGYg4u/GJWUPJfLRc6WTWQtX4o5KIigoSMMrd7jys/n1JuzyT+4n5BR41RJSEREChOBo0eP4nA4iuwLDg4mODi48P2yZctYt24dr776Kg6Hg/bt27Np0ybMfz2oveWWW3j99depX78+b7/9Nna7nQceKCgdf+rUKRYvXkxaWhqTJk1i+PDh3HPPPXTs2JEvv/ySzZs388wzz7jnGt1yVjGGy4V/RhqhyYf/eh0hKDUFk8uFC8gMrcyx2o1Ji6xOWkQ1soLD4Iynr2YfH6D8JQKWyuGEPPBPcjZtIGvl56S+9CyBffvjfU1L9Q4YwJWbS+bHi8nd/j1ejZoQNGgY5gBjS+AWqSQ0/01C//mwhpKJiAgAQ4YM4fDhw0W2Pfjgg4wdO7bwfUpKCnXq1AHAYrEQFBREWloaYWFhQMHQoKioKAAiIyP58ccfAUhMTKRHjx7UqVOHuLi4s84VGRlJcnKy265NiUAZZs63E3L8WOFNf2jyYXxybADke3mTFl6NP6+5ibSIaqSFVyPfp3TGXXsik9mMX4fOeDVuSuaS98l4fz7ev/xEYN/+bittKmfLTzpGxsK3caQk4d+9F3433+IxE3TNAYEE3/NXJaH5cwkdrUpCIiIC77///jl7BP7uzEE2WVlZRR42ulyuwv1n7qtZsyY///wzS5cuZdKkScycORMA518VJP9+npKmRKAM8c1ML/K0P/hEEmbXX78owZU4XqMuaRHVSY2oTmboVeAhN1iexBpRhZDRD5G98Stsq1eS+ucfBN4xEJ8WV7ZirVxczvbvyfxoESZvH4L/MQbvBo2MDuks1ip/VRKap0pCIiJSoGrVqhdtExERwY4dOwDIzMwkPT2dkJD/H4ZcuXJlDhw4QMOGDUlISCAi4v8XP7VYLPTr149XXnkFu91OREQECQkJ1K5d+6y2JU2JgIcyOR0EH08qvPGvlHwYX1smAA6LlVPhVdnfojWpEdVJC6+G/a/FyOTiTBYL/p1vxbtpczIWv0vGwrfIu+4GAvrcidlf32NJc+XbyVq+lJwtm7DWqUvQkHs8puTsuXg3USUhERG5NNHR0cycORObzUZ8fDwdOnRg5cqVJCcnc88999CpUyfi4+OpV68eW7du5dlnn2XVqlWEh4fTsmVLtmzZgq+vL15eXoVtb775ZuLj4+nbt6/b4lYi4KFafPMF1f7cBUB2YDAnq9QsnNSbERaOy1x6C3aVV9Yq1Qgd+zC2r1aR/dUq7H/uIbDfYLybNDM6tHLDcfIEGe++Q/6hg/h17IJ/j96lutjc5fJtfzP5ScfI/moV1sgq+KiSkIiIXEBYWBgjR46kf//+heVD16xZUzi3YNSoUUyYMIHFixcTExND48aNCQ4OZurUqSQmJmIymXjhhRcAGDBgAJMmTSImJobWrVtz8803uy1uVQ26BKVZNSjoRBL+GWmkRVQj1790xrCP79CElJSMUvksT5N/KJGMJe/iOHYUn1ZtCLjtdsy+fkaHVabl7dpJxuJ3weUicMAQfJqXreFXqiQkIlKxXUr50LJKg189VMZVkSTVblRqSUBFZ61Rk9Bx/8Lv5lvI/T6etJeeI2/v70aHVSa5HA6yVi4nfd4czJXCCB33rzKXBMD/VxIyB4eQPv9NHGmpRockIiJSopQIiPzFZPUioOdthIwZj8nLi/S5s8hcuqRwlWW5OGd6OulvziZ73Rp8bryJ0AfHY6kcbnRYl62gktBIyMsjff5cXHn6XRARkfJDiYDI33jVqkPo+En4tr+ZnO82k/rf6dj3/WF0WB7P/udeUl95HvvB/QQOuIugfoMweXkbHdYVO11JyHHkMBmL38PlLN0hiSIiIu6iREDkHExe3gTedjshD/wTgFNvzCRz+VJc9vK34NqVcjmd2Nav4dSc/2H29SV07MP43nCj0WGVKO8mzQiI6UPeLz9hW7PS6HBERERKhKoGiVyAV936VJrwKFkrlpHzzXrsu38lcOBQTRz9i9NmI2Pxu9h/24n3NdcR2G8wZt/yuXCdb4ebyU86SvbaL7FGRKqSkIiIlHnqERC5CJOPD4G39yf4H2Nw2e2cmvVfsr5YjivfbnRohrInHiTtleex7/mNgNh+BA25p9wmAQAmk4nA2wdgrVOPjLj3sR/cb3RIIiIiV0SJgEgxeTdsTOjEyfi0akP2+jWkvfoi+YcSjQ6r1LlcLrK3bOLU7JfB5SJk9EP4RXd06xLonqKwklBQsCoJiYhImadEQOQSmH39CLpzMMEjRuKyZZH2vxlkrf4Cl8NhdGilwpWbS+aihWQtXYJX/QaEPjSpwg2TMgcGETzidCWhN1VJSEREyiwlAiKXwbtJc0InPobPtdeTvWYlaf+bQf7RI0aH5Vb5ScdImzmD3J9+wL9bDMEjHsAcUL4XWjkfa5VqBA0ZhuPIITKWqJKQiIiUTW5NBOLi4ujVqxcDBgwgMfHcQyieffZZhg4d6s4wRNzC7B9A0KC7Cbr7PpxpaaS9+iK2davLZe9A7vbvSZv5Is6sTILvG41/1+6YzBX7OYJ3k+b4x8SSt0OVhEREpGxyW9WgEydOMHfuXJYvX862bduYPn06s2fPLtLmzz//5IcffsDf399dYYi4nU+La/CqU5fMT+KwrfyMvF9/IXDAEKwRVYwO7Yq58u1kLf+EnC3fYK1Tl6Ah92AJCTU6LI/h16EzjqRjBZWEIqvgc+31RockIiJSbG57pLdp0yaaNWuGv78/0dHRbN++HecZ3ecul4vp06dz//33n/cc6enpHDp0qMgrOTnZXSGLXDZzYBBBd40oWHjqeDJpL79A9tfryvSQEcfJE5ya/Qo5W77Br2MXQkb+U0nA3xRUEupfUEloiSoJiYhI2eK2HoGUlBTq1KkDgMViISgoiLS0NMLCwgBYv349VatWpVmzZuc9x4IFC5g1a1aRbS1btmTRokVUqlQxxya7W3h4kNEhlG23dMLe6lqOzZ9P5mef4Pz9V6rddx/eERFGR3ZJMn76iSNvvglOJ9XHjiX4ej3pvpCw8ePYP3UqWe++Te0nn8Trr3/nREREPJlbFxRzuVyFP2dlZRWWF8zLy+ONN97gjTfewGaznff4YcOG0bdv3yLbvL29AUhNzSI/v3SftlaEm+SUlAyjQygHLPgMHgGNtpK17GP+fHwKAb1i8W0T7fHj6l0OB7bVK8hetwZLtRoEDx1BbuVw/V5clImAYf/g1Kz/kvDSy4SOHofJ28fooERE5ApYreZy/+DZbYlAREQEO3bsACAzM5P09HRCQkIA+OGHH0hLS+OBBx4gLy+PgwcP8p///IfHH3+8yDmCg4MJDg52V4gibmMymfC94Ua86jck88MPyPrkQ/J++ZnA/kOwVPLMp8XO9HQyPpiP/c+9+Nx4E4Gxd2Dy8jY6rDLDWqUaQYOHkz5/LhlL3iNoyD0en/iJiEjFZnKd+di+BJ08eZL+/fuzfPlytmzZwieffEKPHj1ITk7mnnvuKWx36NAhJk+ezLvvvntJ5zeqR+Dlr38r1c8sTeM7NNGTXzdwuVzkfLeZrM8/wWQyE3Db7fi0auNRC3DZ/9xL+vvzceVkE3j7AHxvuNHokMos24avsK34FL+u3QnoFmN0OCIicpnUI3AFwsLCGDlyJP379ycgIIAZM2awZs0aDh8+7K6PFPFIJpMJv7bReDdqTGbcB2R++AG5v/xEYL/BWP7qJTuXb7/dxJw5s8jLy6NevQZMnvwEAQGBxWqTmZnJ9OlTOXBgPy6Xi+7dY7jrruFnfYbL6SR741fYVn6GpXI4Qf8Yg7VqtZL+CioUv46dcSSrkpCIiHg+t/UIuJt6BEqeegTcz+V0kvPt12R9sRyT1UpA7J34tLzhrN6B1NRUhg7tz+uvv03NmlG89tpMbDYbDz/8aLHavPLKi5hMZsaNm0h2djZDh/bn3//+D82bX114vNNmI3PJu+Tt2on3NdcR2G8QZl+/UvsuyjNXvp1Tc2eRn5hIyOhxeNWsZXRIIiJyiSpCj4AGsIqUIpPZjF90JyqNfxRLRBUyFy8kY+FbODPSi7Tbtu07mjRpSs2aUQD07duPNWtWFpmAf6E248Y9zJgx4wA4ceI4dntekd6E/EMHSXvlefJ+/42A2H4EDblHSUAJMlm9CL77PsxBQWTMn4vjVJrRIYmIiJxFiYCIASzhEYSMfqhgZdrdu0h96Tlyd2wv3J+UlERERGTh+/DwCLKysrDZsorVxmQyYbVamTr1Ce6+ewDXXns9UVG1cLlcZG/ZRNqsl8HlImTUOPyiO3rUfIXywhwYRPCIkbhyc8mYPxdXXp7RIYmIiBShREDEICazGf9OXQl96BEslcLIePcd0t+fhzMrC5fr3MPezGZL4c/FafPkk8/w+edrychIZ97bb5C5+F2yli7Bq14DQh96BK9adUr2oqSI05WE8g8fImPJe2V6gTkRESl/lAiIGMwaWZWQByfg3y2GvF9+JvWlZwnLt3PixPHCNsePpxAUFIyf3/8P34mMrHLeNvHxWzh+PAUAf39/bm7Vhl9XrSR3+/f43xpD8L0PYP7bxGNxD++mzfHvGUveju3Y1n5pdDgiIiKFlAiIeACTxYJ/1+6E/vNhzIGBNN6xnZ0/bOPA3t8B+PTTj2nfvmORY1q3bsOvv+4kMfHgWW3WrVvDO+/MxeVykbF1C2sWLaRFcDDB943G/5buqm9fyvw6dsanVRuy16wk96cfjA5HREQEUNWgS6KqQVIaXPn52NZ+yTcfL+G9Q4dwBgZRo05dpkx5miNHDjN9+jTmz/8AgC1bNvHGG7PJz7dTvXoNpkx5muDgEDIyMnjxhf/w546fcNmyaFu3Hg9M/y9eHrqYWUWgSkIiImVLRagapETgEigRkNJkTzxA5uL3cCQfw/fGdvj36oPZ17dYxzpOniDjvXfITzyIX4fO+Pe8DZPFcvEDxa2cmRmkzZwBjnxC/vkvLCGhRockIiLnURESAY0PEPFQXjVrEfrQI/h17ELO1m9J++9z5P2596LH5f22k7RXXsCRkkzQ3fcS0LuvkgAPYQ4MIvgeVRISERHPoERAxIOZvLwI6NWHkFEPgcVC+hszyfz0o3PeQLocDrJWLif9nTmYK4UROu4RfFpcW+oxy4VZq6qSkIiIeAYlAiJlgFedulQaPwnfdh3J2byR1JenY9+/r3C/Mz2d9Ddnk71uDT6t2xL64HgslcMNjFgupKCS0G2qJCQiIoayGh2AiBSPyduHwD798G5+NZlx73PqtVfw69AZr4aNyVzyLs7sbAIH3IXvDTcaHaoUg1/HLjiOHSV7zUqskVXwuaal0SGVK99+u4k5c2aRl5dHvXoNmDz5iSKra1+oTW5uDi+99Dy7d+/C6XTRtGkzJk6chI9P8eboiIiUFeoRECljvOs3JHTCZHxb30T2xq9If3M2ePsQOvZhJQFliMlkIrDfQKy165Kx+D3sf5WBlSuXmprKs88+zbRpL7Bo0VKqVavO66/PKnabBQveweFwMH/+IhYsWERubi7vvjvfgCsREXEvJQIiZZDZ15fAfgMJvm80fjffQui4f2GtWs3osOQSmaxeBA+7D3NQEBnz5+I4lWZ0SOXCtm3f0aRJU2rWjAKgb99+rFmzkjOL5F2ozbXXtmTYsHsxm81YLBYaNmzEsWNHDbkWERF3UiIgUoZ5N2pCQM/bMPv6XbyxeKT/rySUo0pCJSQpKYmIiMjC9+HhEWRlZWGzZRWrTevWbYiKKljn4dixo8TFLeLmm7uW3gWIiJQSJQIiIgazVq1G4OBhBZWE4t6jjC7v4jFcrnNXYjKbLZfUZvfu3xg9+j7uuKM/7dq1L9kgRUQ8gBIBEREP4NO0Bf49biPv5+1kr1lpdDhlWmRkFU6cOF74/vjxFIKCgvHz8yt2m7VrVzF+/BgeeGAsd989ovSCFxEpRUoEREQ8hF+nLvhc3xrbmpXk/rzd6HDKrNat2/DrrztJ/GsC9qeffkz79h2L3Wb9+rW88soMXn55Frfe2r10gxcRKUUmVxntg05NzSI/v3QX4gkPD+Llr38r1c8sTeM7NCElJcPoMEQqNFe+nVNzZpF/OJGQUQ/h9ddkVrk0W7Zs4o03ZpOfb6d69RpMmfI0R44cZvr0acyf/8F52wQHhzBwYF8yMzOoXDmi8HwtWlzDxImTjLocETGA1WqmUqWAYrePi4tj4cKFBAQEMGPGDGrWrFm47+TJk0yYMIGUlBR69uzJmDFjsNvtPP/88/zwww84nU6eeeYZrr76auLj4xkzZgzVq1cHYPjw4fTt27fErw+UCFwSJQIiUhqcmRmkzZwBDgch/3wYS0io0SGJiFQ4l5IInDhxggEDBrB8+XK2bdtGXFwcs2fPLtw/bdo0ateuzaBBgxg0aBBTp07Fz8+P7du3Exsby4YNG5gzZw6LFy8mPj6eTz/9lOeee85dl1ZIQ4NERDyMOTCI4OH348zJJmP+m6okJCJioKNHj3Lo0KEir/T09CJtNm3aRLNmzfD39yc6Oprt27fjdP7/A+uNGzfSpk0bLBYL3bp1Y8OGDdSqVYs+ffpgMpm4/vrrOXLkSGH7kJCQUrk2JQIiIh7IWq06QYOHk384kYy491VJSETEIEOGDKFLly5FXgsWLCjSJiUlhTp16gBgsVgICgoiLS2tcP/JkyeJiioY6hkZGUlycnKR43/++WeaNm1a+H7z5s3ExsYyatSos9qWJKvbziwiIlfEp1kLHD16Y/tiOdmRVfC/pYfRIYmIVDjvv/8+DoejyLbg4OCz2p35wCYrKwuTyVRk3+n9f99nt9uZNWsWDz/8MADXXHMN06dPp169erzwwgu88sorPPvssyV6TaepR0BExIP5depaUElo9ReqJCQiYoCqVatSo0aNIq+/JwIREREkJCQAkJmZSXp6epHhPZUrV+bAgQMAJCQkEBFRUIzA5XIxZcoU2rdvT6tWrQDw9fWlWbNm+Pr6cscdd7Bv3z63XZsSARERD2YymQjsNxBrrTpkLHmX/EMHjQ6pXHDl5WHf9wfOTBVIEJErFx0dza5du7DZbMTHx9OhQwdWrlzJvHnzAOjUqRPx8fE4HA62bt1Kx44F5YpnzJiB3W5nzJgxhef64osvsNlsOBwO1q1bR/Pmzd0Wt4YGiYh4OJPVi+Dh/yDt1RdJnzeXkH/+C0spTSQrL1x2O/aD+7H/sQf7n3vJP7gfHA5MPj74deyKX8ebMXn7GB2miJRRYWFhjBw5kv79+xeWD12zZg2HDx8GYNSoUUyYMIHFixcTExND48aN2bBhA2+99RbNmjWjT58+AEyZMgWLxcKIESM4fvw4DRo0YPr06W6LW+VDL4HKh4qIkfKPHCZt9n+xRlYhZNQ4TF7eRofksVz5+eQnHvzrxn8P9gP7Id8OJhPW6jXxqtcAa1Rtcrd/T97OnzEFBRPQrSc+N7TBZLEYHb6IeIBLXUegLFKPgIhIGXG6klDGgjfJWPI+QUOGF5lwVpG5HA7yDycWPvG3J+wDex6YTFiqVse3bTRe9RvgVaceZj//wuN8rr4W+/59ZH3+KZkfLSb7mw0E9IzFq0kzfbciUu4pERARKUNUSaiAy+nEceQweX/uwf7HHvIT9uHKzQHAElkV39Zt8KrXAK+6DTAHXPiJnlftuoSMGU/ezp/J+mI56fPmYK1bn4BeffCqWas0LkdExBBKBEREyhi/Tl1xJB3DtvoLtiUn887qleTl5VGvXgMmT36CgIDAIu2//XYTc+bMOqtNbm4OL730PLt378LpdNG0aTMmTpyEj4+vQVd2fi6nE8exowVP+//cg33fH7iyswGwhEfgc931eNVviFe9BpgDgy75/CaTCZ8W1+LdtAU5323GtmYlp2bOwPvalgR0743lqsolfUkiIobTHIFLoDkCIuIpXHY7B199kdErPmP2Cy9Tu1UbXnttJjabjYcffrSwXWpqKkOH9uf119+mZs2oIm3mzn2NpKRjPP74v3G5XEyd+gQ1a0Zx330PGHhlBVwuF47kpIKb/j/3Yv/zD1xZmQCYwyoXDPOpV/CyhISW+Oc7c7LJ3vAV2V+vA6cT35va49+l+0V7F0Sk/NAcARER8UgmLy9+a9CYBqHfELxqBY6GTejbtx/Dhw9i4sRJhePbt237jiZNmlKzZsGKlme2ufballSpUhWzuaCSdMOGjUhIcF+96gtxuVw4TxzH/uce8v7YU3Djn5EOgDm0Et6Nmxbc+NdviKVSmNvjMfv6EdC9F75to7Gt/oKcTRvJ3RaPX+db8YvuoIna5cT5esuK06Ys9aiJnI8SARGRMiolPZ2q17TEmW0jY8FcKt8/hqysLGy2rMKbmaSkJCIiIguPCQ+PKGzTunWbwu3Hjh0lLm4RjzzyeKnF7zh5ouBp/18TfJ2n0gAwBwfj/dcwH696DTBfVdmwibuWkFCC7hyMX/tOZK1Yhu2LZeR8+zX+3Xvhc90NmMxajqesSk1N5dlnny7SW/b667PO6lE7X5sFC97B4XAwf/6iwh61d9+d7xE9aiLFpURARKSMcrmcmAMCCOoxgIyFb5H54SIAzGZLkTbncmab3bt/47HHHuaOO/rTrl17t8XrOJWG/Y+/xvj/sQdn6kkATAGBhTf9XvUbYAmP9LiKPdYq1Qi5dxR5f/xO1ufLyFz8LtlfrycgJhbvho2NDk8uw4V6y8paj5rI5VIiICJSRkVGVmHXrp34NL8aR/de7P/0I4J8/fDz8zurzWnHj6cQFBRc2Gbt2lW89NLzjB//CLfe2r1E43NmpP/1xH8veX/uwXk8BQCTnz9e9erj1aFzwY1/ZFWPu/E/H+/6jfD658Pk/vwjtpWfkf7mbLwaNiEg5jas1WoYHZ5cggv1lpWFHjWRkqBEQESkjGrdug2zZr1CYuJBatx8C2s//ZhWQYHk7tiOz9XXndWmZs0oPv30Y9q3L1jafv36tbzyygxefnkWjRs3veJ4nFmZ2P/8o3CCryPpGAAmX1+sderj16aglr+lavUyPaTGZDbje90N+LS4hpzN32D7ahVpr7yAT8tW+HfvhSW0ktEhSjEUp7fMk3rURNxBiYCISBlVqVIYjz32JFOmTCI/3061qtX4Z7v2bH/zNV4/mcqC9z86q0316jWYMuVpAObMmQ24mD59WuE5W7S4hokTJxXr853Ztr9u/AuG+ziOHinY4e2NV+16+FzfGq/6DbFWq1EuV+s1Wb3w69gZn1ZtyF63muxNG8n9eTt+7Tvhd/MtmM/omRHPc7HesuK0cWePmkhpUCIgIlKGtW0bTdu20YXvnRnpBMycwcuRVXCmn8IcHHJWm9MWL/7kkj7LmZNDfsKfhTf++YcPgcsFVi+8atfBp3svvOo1wFqzVrm88T8fs78/Ab364HtTe2xffk72+jXkxH+L/y3d8W0Tjcmq/2o90YV6y4rTpqR71ESMoHUELoHWERCRsiD/yCHSZr+MNbIKIaPGXXapS1deHvb9+/6a3LuX/EMHwekEixVrrdp41WuAd70GWGvVxmT1KuGrKLvyDyWSteJT7H/swRxWmYCevfG++royMw+iItmyZRNvvDG7SG/ZkSOHmT59GvPnf3DeNsHBIQwc2JfMzAwqV44oPN+l9KiJ56sI6wgoEbgESgREpKzI3fkzGQvewvvalgQNHl6sm1CX3Y79QML/P/E/eAAcDjCbsdasVbhyr1etOpi8VUf/QlwuF/bfd5G1YhmOY0ex1qxFQK8+eNWtb3RoIlJMFSERUH+liEg55NP8Ghw9emNb+RnZkVXx73r2+GVXfj75iQf+v6TngQTIzweTCWuNmvi1v7ngxr9OPUw+PgZcRdllMpnwbtwMr4ZNyP1hK7YvP+fU66/i3bQF/jG3YY2oYnSIIiJKBEREyiu/m2/BkXQM26oVWCKq4N2sBfmHEwsW8PpjL/b9+8CeByYTlqrV8W3bHu/6DbHWqaeJriXEZDbj26oNPte0JPub9WSvX0vaS8/h27ot/rf0xBwcbHSIIlKBKREQESmnTCYTgf0G4TieQsaiBZgsVly5OQBYqlTFt3Xbvxbyqo/Zv3x3fxvN5O2Nf5du+N54E7a1X5KzZRM5P27Dv2MX/Dp2UY+LiBhCiYCISDlm8vIiePg/yPhwEZaQULzqF6zgaw4MMjq0CskcGERgnzvxa9eRrC8/w7ZmJdlbNuF/a098W7etUNWWRMR4mix8CTRZWERESpL9QAJZn39K/v59WCIi8e8Zi3fT5qow5AFcebnY9/2Jfe/vmPz98e/SzeiQpJRpsrCIiIi4jVetOoSMfoi8X3dg+2I5GfPnYq1bn4CYWLyiahsdXoXicjgKJs/v3UPeH7+TfyChoGrWX7waNNKfiZQ7SgREREQMZDKZ8Gl+Dd5NmpOzdQu21V9w6n8v4X3NdQR0742lcrjRIZZLLpcLR9Ix7Ht/x/7H79j//KNgDo3JhKVajYKqWQ0aYa1WndQZz2JbtYKQf4wxOmyREqVEQERExAOYLBb82kbjc90NZG/8iuyN68jbuQPftu3x79oNc0Cg0SGWeY601IIb/72/k/fHHlwZ6QCYK4fjc931BU/96zXEHFB0OIhfp67YVnyKfd8fWgtCyhUlAiIiIh7E7OtLQLcY/NpGk7X6C3I2byT3++/wu/kW/Np3uuyVoisip81WsEDeX0/9HSnJAJgCAvFq0BDvBo3xqt8QS9hVFzyP303tyf56HVmrVhDywD81h0PKDSUCIiIiHsgcHEJQv0H4te+E7Yvl2FZ+Rs633+DfvRc+LVthMpsv6XzffruJOXNmkZeXR716DZg8+QkC/tbLcL42ubk5vPTS8+zevQun00XTps2YOHESPj6+JXnJV6xgdex9hU/98w8lgssF3t541a2P743t8GrQCEuVqpf0/Zm8vfHvfCtZyz7C/scevBs0cuNViJQeVQ26BKoaJCIiRsn7cy+2zz8l/9BBLFWrExATi3ejJsU6NjU1laFD+/P6629Ts2YUr702E5vNxsMPP1qsNnPnvkZS0jEef/zfuFwupk59gpo1o7jvvgfcdbnF4nI6cRw5RN5fN/72hH2QbwezGWtUbbwaNCpYJC+qNibrlT37dNntpD4/FXNoJULGjFevQAWgqkEiIiLiEbzrNcBr7ETydmwna+VnpL/1Gl4NGhMQE4u1eo0LHrtt23c0adKUmjWjAOjbtx/Dhw9i4sRJhTe0F2pz7bUtqVKlKua/nqI3bNiIhIR9brzac3O5XDiPp5D3x56/hvvswZVtA/5aJK9tO7zrN8Jatx5m35JdHdvk5YVfl25kLV2C/fddeDduVqLnFzGCEgEREZEywmQ243Pt9Xg3v5qcLZuwrf2StFdfwKdlK/y7xWCpFHbO45KSkoiIiCx8Hx4eQVZWFjZbVuHwoAu1ad26TeH2Y8eOEhe3iEceedxNV1mUMyP9/2/89/6OMy0VAHNoJbybX13w1L9eQ8zBwW6PxbdVG7LXr8G26gu8GjVVr4CUeUoEREREyhiT1Qu/9jfjc8ONZK9bQ/amDeT+/CN+0Z3w63wLZj//Iu1drnMPpTWbLZfUZvfu33jssYe5447+tGvXvgSu5GzOnBzyE/4gb2/Bzb/j2BEATH5+eNVriNfNt+DdoBHmyuGlfiNuslrxv6UHmXHvk/frL/g0v7pUP1+kpCkREBERKaPMfv4ExMTie1N7bKtWkL3xK3K2fot/1+74to3GZPUCIDKyCrt27Sw87vjxFIKCgvHz+//hMxdrs3btKl566XnGj3+EW2/tXmLX4HI4yD+4v2Cc/x+/k39gPzidYLXiVbsuPj1649WgMdbqNS55grQ7+LRshW3damyrVxSsAu0BMYlcLrcmAnFxcSxcuJCAgABmzJhBzZo1C/e9++67fPLJJ6SlpXHbbbcxbtw4dbGJiIhcBkulMIIGDsWvfSeyViwja/lSsjdtJKBHb7yvaUnr1m2YNesVEhMPUrNmFJ9++jHt23csco4LtVm/fi2vvDKDl1+eRePGTa8oVpfLhePYUex7d5O393fyE/7ElZsLJhPWGjXx69iloJ5/7ToeWSrVZLEU9AosWkjeLz/jc811RockctncVjXoxIkTDBgwgOXLl7Nt2zbi4uKYPXt24f6tW7dy/fXXk5+fT9euXVmyZAnVqlUr9vlVNajkqWqQiEj5kPf7b2St+BTH0SNYa0YRENOH75OTeOON2eTn26levQZTpjzNkSOHmT59GvPnfwDAli2bzmoTHBzCwIF9yczMoHLliMLPaNHiGiZOnFSseBypJ4su5JVZ8H+NJTwCr/qN8GrQEK96DTD7l40KLS6nk7T/PgcuCJ04Wb0C5VRFqBrktkRg2bJlrFu3jldffRWHw0H79u3ZtGlTYcUBgPz8fHbu3Mnjjz/OsmXLsP6ttFd6ejrp6elFtnl7exMREaFEwA2UCIiIlB8up5PcH7dh+/JznKfS8G7aHP+et2GNrOr2z3basrD/sfevG//fcR5PAcAUGIR3g0YFT/wbNMISWsntsbhL7s/byXjvHQIH3Y1vy1ZGhyNuUBESAbcNDUpJSaFOnToAWCwWgoKCSEtLIyzs/ysa3H///WzZsoUFCxaclQQALFiwgFmzZhXZ1rJlSxYtWlTu/2CMEh4eZHQIIiJSUnp0xdmlAydXr+bEihWkvfQcoR06ULlvX7xCQ0vsY5x5edj27MG2axdZu3aRc+AAuFyYfX3xb9SIgFtvwb9pU3yqVy83w4BdnaNJ2LiG3HWrqNG1IyaL5eIHiXgYt84ROLOzISsr66y//O+88w779u1j7NixLFiwgMqVKxfZP2zYMPr27Vtkm7d3wXhBo3oEyjv1CIiIlEM3diS0+fXY1q4ibdM3pH27Bb+OnfHr2AWz76WvDuxyOsk/dLCwlr99/z7IzweLBWtUbfxv6YFXg0ZYa9bCZLHgADKAjOOZJX5pRvLu3J2MBW9y6Muv8G3d1uhwpISpR+AKREREsGPHDgAyMzNJT08nJCTkrHZ169blpptuYtOmTfTp06fIvuDgYIJLoS6wiIhIeWcOCCQw9g782nUg68vPyF77JTnfbcb/1p74tm57wSfaLpcLR0ryXzf+v2P/cy+u7GwALFWr4XtT+4IhP3XqY/LxKa1LMpx3sxZYa0RhW/slPi1bXfHqxSKlzW2/sdHR0cycWbA8eXx8PB06dGDlypUkJyfTp08fPvzwQ0aMGEFOTg6bN2/m1ltvdVcoIiIi8hdL5XCC7xqBvcN+sj7/lKylS8j5Zj3+PW/Du9nVhb33zvRTf9Xy3439jz04T6UBYK4UhneLawtu/Os3xBxY/nvLz8dkMuHfrSfpb79Bzrbv8GsbbXRIIpfEbYlAWFgYI0eOpH///oXlQ9esWcPhw4cJDg4mOzubfv36cerUKW6//XZatdJEGxERkdLiFVWbkFHjyNu1E9sXy8hY8BbWOnWxVq+Jfe8eHElHATD5+xdU9qnfsGAhr6sql5tx/iXBq1FTrLXqkP3VKnxvuBGTl5fRIYkUm9uqBrmbqgaVPFUNEhGpmFwOBzlbt2Bb/QWu3By86tTDq0EjvOs3wlKtuspjXkTeH7+TPmcWAbF34BfdyehwpIRc6hyBC62fdfLkSSZMmEBKSgo9e/ZkzJgx2O12nn/+eX744QecTifPPPMMV199NQ6Hg6lTp/L9999Tv359nn/+eXwvYy5PcehvtoiISAVnsljwaxtN2JRnuGrqC4T8Ywz+nbpirVFTSUAxeNdvhFe9Bti+Wo0rL8/ocMQAJ06cYO7cucTFxTF69GimT59eZP9rr71G165dWb58ORs3bmT37t0cOXKE5s2bs3TpUh566CGeffZZANatW0dqaiorVqygevXqLFmyxG1x62+3iIiIAAUJgSa8Xh7/bjG4MjPI/vYbo0OREnb06FEOHTpU5PX3da42bdpEs2bN8Pf3Jzo6mu3bt+N0/v/IlY0bN9KmTRssFgvdunVjw4YN1KpViz59+mAymbj++us5cuQIABs2bODGG28EoEePHmzcuNFt16a/7SIiIiJXyKtOPbwaNiZ7w1p827S7rLKs4pmGDBnC4cOHi2x78MEHGTt2bOH7i62fdfLkSaKiogCIjIzkxx9/LHK+n3/+maZNmxaeq27duoVtk5OT3XNhKBEQERERKRH+3WI49b+XyNm8Ef8u3YwOR0rI+++/j8PhKLLtXOXtL7R+lsvlKtz/9312u51Zs2bx8MMPF2473ZtwrnW4zuXXX3/l6NGjdO3aFYCcnJxizSvQ0CARERGREuAVVRuvJs3J3rgO51/rLEjZV7VqVWrUqFHk9fdEICIigoSEBODc62dVrlyZAwcOAJCQkEBERARQkCBMmTKF9u3bF1bQPPNcZ7Y9n5deeol3332XGTNmAAVDmR566KFiXZsSAREREZESEtCtJ65sG9nfrDc6FClF0dHR7Nq166z1s+bNmwdAp06diI+Px+FwsHXrVjp27AjAjBkzsNvtjBkzpvBcp9sCxMfHF7Y9n7Vr1/Lcc88V9gBUrVqVY8eOFStuJQIiIiIiJcRavSbeza8h55v1OLOyjA5HSsmZ62fNnTuXSZMmkZSUxKFDhwAYNWoU69at47bbbqNr1640btyYDRs28NZbb7F//3769OlDbGws27Zto3PnzlSuXJmYmBiOHTvGgAEDLvjZfn5+nDhxonAI0Y8//ohXMdez0DoCl0DrCIiIiMjF5B87Qtp/p+PXqSsBPW8zOhy5TJe6joBRfvnlF55++mn2799PgwYNOH78OC+//DLNmze/6LGaLCwiIiJSgqxVquF9zXVkb9qIX4ebMQcGGR2SlGMtWrRg8eLF7N+/H6fTSZ06dbBYLMU6VkODREREREqY/y09Id+Obf0ao0ORcu6ee+7BarVSv359GjZsiJeXF0OHDi3WscXqEcjIyGDBggX8+uuv+Pr60qZNG+644w6sWnRERERE5CzWiEh8WrYi59tN+HXoguWMCjIiJeHnn3/mp59+Yt++fSxcuLBw+7Fjx0hJSSnWOYrVIzB+/HgyMjIYPHgwd955J9u2bePf//73ZQUtIiIiUhH439IDnA6y1682OhQph3x8fLBarTgcDtLT0wtfderUKZIYXEixHukfP36ct956q/B927Zt6d279+VFLSIiIlIBWK6qjE+rNuR89y1+HbtgqRRmdEhSjjRu3JjGjRtTv359brzxxss6R7ESgWuvvZbdu3fTuHFjoGDp49M/i4iIiMi5+XfpRu73W8n+ajWB/QYaHY6UQ3Xr1mX27NkcOXKkcEVigOeee+6ixxYrEdi0aRMfffQR/v7+uFwu8vLyMJlMtGrVCpPJxNatWy8/ehEREZFyylIpDN8b25Lz3Wb8bu6K5arKRock5czYsWNp164d+/fvZ/jw4fz666/FnsdbrFZr1669ogBFREREKiq/zt3I2fodtrVfEjTgLqPDkXImLy+PsWPHkp2dTY0aNbjlllsYOnQoDz744EWPLXbZn507d/Ltt9/idDpp164dLVq0uKKgRURERCoCS0gIvm2jydm0Af/Ot2IJjzA6JClHatSowf79+4mJieHll1+mY8eOZGQUb4HYYlUNWrRoEU899RTe3t74+vry73//mw8++OCKghYRERGpKPw73wJWL2xrVhodipQzjz32GJUrV6ZZs2b079+fffv2MXXq1GIdW6wegQ8++IAPP/wQX19fAAYMGED//v0ZPHjw5UctIiIiUkGYA4Pwi+5A9oav8Ot8K9YqVY0OScqJN998kyeeeAKArl270rVr12IfW+yVhR0OR+HPZ85IFhEREZGL8+vYFZO3D7bVXxgdipQjKSkp7N+//7KOLVaPwL333kvfvn3p0KEDAN988w2jRo26rA8UERERqYjMAQH4tu9E9tovyT98CGv1GkaHJOVAaGgoffv2pU2bNlgslsLts2bNuuixxUoE+vTpw9VXX82WLVsAGDRoEPXq1bvMcEVEREQqJr8ON5OzeSO21SsIvmek0eFIOdCrVy969ep1WccWKxEYOHAgixcvpm7dupf1ISIiIiICZj9//Dp0xrZqBfaD+/GKqm10SFLGtW7d+rKPLVYicOONN7Js2TJiY2Mv+4NEREREBHyjO5H9zQZsq74g5B+jjQ5HyqjTC/va7XZcLhfe3t4AZGdnU61aNVatWnXRcxQrEdiwYQNvvvkmU6dOxWKx4HK5tKKwiIiIyGUw+/rid3NXbCuWYU/Yh1cdjbiQS7dt2zYAJkyYwOTJkwkPDwdg7969vPvuu8U6R7ESgQ8//JAVK1bw66+/4uPjQ9u2bYmOjr7MsEVEREQqNr+bOpC9cR22VZ8T8sA/jQ5HyrA9e/YUJgEADRo04KeffirWscVKBCZPnkxeXh7t2rXDYrEwZ84ctm3bxvjx4y8rYBEREZGKzOTtjX/nW8la/jF5f+zBu35Do0OSMuqaa65h4sSJ9O/fn6ysLL766iuqVatWrGNNLpfLdbFGvXv35rPPPit8n5eXR58+ffjiC+Pq4KamZpGfX7rrGYSHB/Hy17+V6meWpvEdmpCSUrwlqUVEROTKuOx2Up+firlSGCGjH8JkMhkdkpzBajVTqVKA0WFclN1u58MPP+S7777DbrdzzTXXcNdddxEYGHjRY4vVI9CoUSMOHTpEjRoF9W7z8vKoXbv2FQUtIiIiUpGZvLzw63wrWZ/EYd+zG+9GTYwOScogLy8vBg8ezODBgy/52GIlAomJiQwYMICIiAgATp06hdPppG/fvgB88sknl/zBIiIiIhWdb+u2ZG9Yi23V53g1bKxeAblkX3zxBS+99BLJycn4+fmRk5NDjRo1ijVyp1iJwH//+98rDlJEREREijJZrfh37U7mhx+Q99tOfJq2MDokKWNeffVVFixYwMyZM/n3v//NoUOH+Pjjj4t1bLESgerVq19RgCIiIiJybj7Xt8a2bjW2L1fg3bgZJrPZ6JCkDAkNDaVGjRo0bdqULVu20KVLF+Lj44t1rH7TRERERAxksljwv6UHjqOHydv5s9HhSBnTvn17fv31V/r06cPMmTO54447qFWrVrGOVSIgIiIiYjCf627AEhGJbfUXuJylWxVRyrb777+fvXv3MmvWLNq2bUuPHj2YMWNGsY5VIiAiIiJiMJPZXNArkHSM3J9/NDocKUMmT57M2rVrqV+/PvXq1WPjxo3Mnj27WMcWa46AiIiIiLiX99XXYflqNbbVX+Bz9XWYLBajQ5IyYM+ePUXW+4qNjaVPnz489NBDFz1WPQIiIiIiHsBkNuPfrSfO4ynk/rjN6HCkjGjevDmHDx8ufJ+Xl0edOnWKdax6BEREREQ8hHezq7HWqIlt7Zf4tGylXgE5rz59+mAymcjMzKR///5F1vu66qqrinUOJQIiIiIiHsJkMuF/awzp77xB7rbv8G3TzuiQxEMVdx7AhSgREBEREfEgXo2bYo2qje2rVfjc0BqT1cvokMQDlcQ6X5ojICIiIuJBTCYT/t1icKalkhP/rdHhSDmmREBERETEw3g1aIS1bn2y163GZc8zOhwpp5QIiIiIiHgYk8lEQLcYnOnp5Hy7yehwpJxSIiAiIiLigbzq1serQSNs69fgys01Ohwph5QIiIiIiHgo/24xuLIyyd680ehQ5CLi4uLo1asXAwYMIDExsci+kydPMnz4cGJiYopU+/n444/p0qUL//nPfwq3LV26lLZt2xIbG0tsbCybN292W8xKBEREREQ8lFetOng1bkr2hq9wZmcbHY6cx4kTJ5g7dy5xcXGMHj2a6dOnF9n/2muv0bVrV5YvX87GjRvZvXs3AK1atSI2Nvas8w0ZMoRly5axbNky2rVzXwlZJQIiIiIiHiygWwyubBs536w3OpQK6ejRoxw6dKjIKz09vUibTZs20axZM/z9/YmOjmb79u04nc7C/Rs3bqRNmzZYLBa6devGhg0bAIiKiqJGjRpnfWZISIhbr+k0JQIiIiIiHsxaIwrvZleT/c16nLYso8OpcIYMGUKXLl2KvBYsWFCkTUpKCnXq1AHAYrEQFBREWlpa4f6TJ08SFRUFQGRkJMnJyRf8zGXLltG7d2/+9a9/kZmZWbIXdAYtKCYiIiLi4fy7xZD28i9kb1xHQI/eRodTobz//vs4HI4i24KDg89q53K5Cn/OysrCZDIV2Xd6/9/3/V3nzp255ppriIyM5NFHH2XBggWMGTPmSi/jnJQIiIiIiHg4a9VqeF99HdmbNuDXvhPmwCCjQ6owqlatetE2ERER7NixA4DMzEzS09OLDO+pXLkyBw4coGHDhiQkJBAREXHec4WGhhIaGgrAbbfdxqpVq67sAi5AQ4NEREREygD/W3uA3U72hq+MDkX+Jjo6ml27dmGz2YiPj6dDhw6sXLmSefPmAdCpUyfi4+NxOBxs3bqVjh07nvM8LpeLZcuWYbfbycvLY+PGjbRo0cJtcatHQERERKQMsEZUwee6G8j+9mv8OnTGfI7hKWKMsLAwRo4cSf/+/QkICGDGjBmsWbOGw4cPAzBq1CgmTJjA4sWLiYmJoXHjxgDExsZy6tQpcnJy2Lp1Kx9++CGZmZkMHjyYEydO0Lp1awYPHuy2uE2uMwc0lSGpqVnk5zsv3rAEhYcH8fLXv5XqZ5am8R2akJKSYXQYIiIich6O4ymkvjgN35vaExjbz+hwyjWr1UylSgFGh+FWbh0adKGFFdauXcvAgQPp1q0br732mjvDEBERESkXLJXD8bnhRnK2bMaRlmp0OFLGuS0RuNDCCi6Xi3379jFv3jyWLVtGXFwc+/btc1coIiIiIuWGf9fugIvsr1YbHYqUcW5LBC60sILJZOL+++/Hz88PX19fmjZtyrFjx846R3p6+lkLOFys7qqIiIhIeWapFIZv67bkbP0Wx8njRocjZZjbJgufb2GFsLCwIu3sdju///47DRs2POscCxYsYNasWUW2tWzZkkWLFpX7MVtGCQ9XOTIRERFPF9r/Dv7c9h3OTeuocu+9RocjZZRbqwZdaGGF0xYvXkybNm2oXLnyWfuGDRtG3759i2zz9vYGjJssXN5psrCIiEhZYMWnTTSnNn+NuW0nLOHnr0svl6ciTBZ2WyJwsYUVAL755hs++eQTFi5ceM5zBAcHn3PlNhEREZGKzv/mW8iJ34xtzUqCBg8zOhwpg9w2R+BiCyvs2LGDp59+mv/9738EBga6KwwRERGRcskcHIzfTR3I/ekH8pOOGh2OlEFuSwTOXFhh7ty5TJo0iaSkJA4dOgTA/fffT35+PmPHjiU2Npbnn3/eXaGIiIiIlEt+N3fF5O2NbfVKo0ORMkgLil0CLSgmIiIinibry8/J/moVoeMnYa1Ww+hwyo2KMEfArQuKiYiIiIh7+XXojMnXD9vqL4wORcoYJQIiIiIiZZjZ3x+/Dp3J+/UX7IkHjQ5HyhAlAiIiIiJlnG/7jpj8/bGtXmF0KFKGKBEQERERKePMvn74deqKffcu7PsTjA5HygglAiIiIiLlgF+7DpgCg7Ct+tzoUKSMUCIgIiIiUg6YvH3wv7kr9j/2kPfnXqPDkTJAiYCIiIhIOeHbNhpzcAi2VZ9TRivESylSIiAiIiJSTpi8vPHrfCv5Cfuw79ltdDji4ZQIiIiIiJQjvje2xRxaCduqFeoVkAtSIiAiIiJSjpisXvh37UZ+4gHsv/1qdDjiwZQIiIiIiJQzPje0wRxWmazV6hWQ81MiICIiIlLOmCwW/G/pjuPwIfJ2/mx0OOKhlAiIiIiIlEM+LVthCY/AtvoLXE6n0eGIB1IiICIiIlIOmcxm/G/tiePYUfJ2bDc6HPFASgREREREyinvq6/DUqVqQa+Aw2F0OOJhlAiIiIiIlFOFvQIpyeRu/97ocMTDKBEQERERKce8m1+DpXoNbGu+VK+AFKFEQERERKQcM5lMBNwag/PkcXK/jzc6HPEgSgREREREyjmvJs2wRtXGtvZLXPl2o8MRD6FEQERERKScM5lM+N/aE2daKjnxW4wORzyEEgERERGRCsCrYWOsdeqSvW4VLnue0eGIB1AiICIiIlIBmEwm/Lv1wpmeTs6WTUaHIx5AiYCIiIhIBeFdrwFe9RtiW78WV16u0eGIwZQIiIiIiFQg/t1icGVmkL35a6NDEYMpERARERGpQLxq18WrcVOyN6zFmZNtdDhiICUCIiIiIhWM/60xuGw2cr7ZaHQoYiAlAiIiIiIVjFfNKLybtSD763U4bTajwxGDKBEQERERqYD8b43BlZNN9tfrjA6lXIiLi6NXr14MGDCAxMTEIvtOnjzJ8OHDiYmJYfbs2YXbP/74Y7p06cJ//vOfwm05OTn885//JCYmhqeffhqHw+G2mJUIiIiIiFRA1mrV8b76OnI2bcCZlWl0OGXaiRMnmDt3LnFxcYwePZrp06cX2f/aa6/RtWtXli9fzsaNG9m9ezcArVq1IjY2tkjbxYsXU716dVasWMHJkydZv3692+JWIiAiIiJSQfnf2gNXXh7ZG74yOhSPdfToUQ4dOlTklZ6eXqTNpk2baNasGf7+/kRHR7N9+3acTmfh/o0bN9KmTRssFgvdunVjw4YNAERFRVGjRo0i59qwYQNt2rQBoEePHmzc6L55HFa3nVlEREREPJo1sio+115P9uaN+LW/GXNwsNEheZwhQ4Zw+PDhItsefPBBxo4dW/g+JSWFOnXqAGCxWAgKCiItLY2wsDCgYGhQVFQUAJGRkfz444/n/bwzzxUZGUlycnKJXs+ZlAiIiIiIVGD+t/Qg9+cfsa1fQ2DsHUaH43Hef//9s8bpB58jYXK5XIU/Z2VlYTKZiuw7vf/v+87ldG9CcdpeCQ0NEhEREanALOER+FzfmpzvNuFISzU6HI9TtWpVatSoUeT190QgIiKChIQEADIzM0lPTyckJKRwf+XKlTlw4AAACQkJREREnPfzzjzXxdpeKSUCIiJl2LffbmLYsIEMGnQ7U6ZMIus8E/5cLhf/+c+/+eCDdwu35ebm8OyzT3P33QO4667+PPvs0+Tm5pRW6CLiQfy7dgOnk+x1q40OpUyKjo5m165d2Gw24uPj6dChAytXrmTevHkAdOrUifj4eBwOB1u3bqVjx47nPdfptgDx8fEXbHullAiIiJRRqampPPvs00yb9gKLFi2lWrXqvP76rLPa7d+fwLhxo1i3bk2R7QsWvIPD4WD+/EUsWLCI3Nxc3n13filFLyKexBJWGd/WbcnZugVH6kmjwylzwsLCGDlyJP3792fu3LlMmjSJpKQkDh06BMCoUaNYt24dt912G127dqVx48YAxMbGMnPmTD777DNiY2PJy8tjwIABHD16lJiYGMLDw7n55pvdFrfmCIiIlFHbtn1HkyZNqVmzYAJa3779GD58EBMnTioypnTp0jh69uxNZGSVIsdfe21LqlSpitlc8EyoYcNGJCTsK70LEBGP4telGznfx2Nb+yVBdw42Opwy58477+TOO+8sfD9ixIjCnytVqlTYO3CmZcuWnfNcr776askHeA7qERARKaOSkpKIiIgsfB8eHkFWVhY2W1aRdhMmTKJ795izjm/dug1RUbUAOHbsKHFxi7j55q7uDVpEPJYltBK+bdqR+308juMpRocjpUCJgIhIGeVyOc+53Wy2XNJ5du/+jdGj7+OOO/rTrl37kghNRMoo/5tvBYsF25qVRocipUCJgIhIGRUZWYUTJ44Xvj9+PIWgoGD8/PyKfY61a1cxfvwYHnhgLHffPeLiB4hIuWYODsbvpg7kbv+e/ORjRocjbqZEQESkjGrdug2//rqTxMSDAHz66ce0b1/86hLr16/llVdm8PLLs7j11u7uClNEyhi/Tl3AywvbavUKlHeaLCwiUkZVqhTGY489yZQpk8jPt1O9eg2mTHma3bt3MX36NObP/+CCx8+ZMxtwMX36tMJtLVpcw8SJk9wcuYh4qm+/3cScObPITU0l6pdfeLxte0Lq1T9nm7y8POrVa8DkyU8QEBBYuD8p6RgjR97D/PmLCA0NLeUrkEthcp25DFoZkpqaRX7+ucfHukt4eBAvf/1bqX5maRrfoQkpKRlGhyEiIiIGSE1NZejQ/rz++ttUv+oqXrlvKLkBgUx+c+E529SsGcVrr83EZrPx8MOPArBy5ee8/fYcjh07yuefry3TiYDVaqZSpQCjw3ArDQ0SEZFy50oWWnM4HLzyygwGD76DAQP68OmnH5VW2CKGOrMksdk/gD597mD9nt+xJx44ZxsoKFu8Zs1KXC4Xx4+n8M03G3nxxdIpfSlXTomAiIiUK1e60NqyZUs5dOggCxcu4c03FxIXt4hdu3aWVvgihvl7SeIaPW/D5nBw/PNPz9vmzLLFlSuH8+yzL1KnTt3SDFuugBIBEREpVy70xPJMpxda69z5liLbv/56PT173obVaiU4OJguXW5ltSZNSgXw95LEZt+CCmSOvb9j359wzjaFbS+xbLF4BiUCIiJSrlzpQmvJyUWPj4iIJDk52X0Bi3iIc5ckDsIvOATb6hUXaHNpZYvFcygREBGRcuVKn1g6nWfX0DCb9d9leaS5JEWduyRxJ/xu7op97+/Y9/1xxWWLxbOofKiIiEFCKvnjbS2f3el5+Q5OpdoM+ezIyCpFxvRf6hPLvz/xTElJJiIiosTjFGOdnktyZvWb11+fVVj95rT9+xP473+f59dff+Hee+sVbj9zLonNZuOBB+6hYcPGNG3avLQvpcScryTxgQP7eXbnTv63agWhD/zznG2kbFIiICJiEG+rpdyWJB7foYlhn926dRtmzXqFxMSD1KwZdclPLNu378CKFctp16492dnZfPXVah5+eLIbIxYjnGsuyfDhg5g4cRImk6mw3em5JJGRVYoc//XX67ntttvPmktSlhMBgLZto2nbNrrItuAW1zB3yr/J+vQj7Ht/P2ebv9u06Xt3hiklRH2dIiJSrpz5VHPIkH7s2/cHDz74ELt372L48MEXPb5Pn35Ur16D4cMH849/3E1MTCzXXXd9KUQupUlzSS6N7403YQ6thG3VirMm3kvZpR4BEREpd875VDM45JyrLT/++L+LvLdarYwbN9Gd4YkH0FySS2OyeuHfpRuZHy/GvnsX3k2aGR2SlIDy+xsrIiIich5XWv2mIs4l8WnVBnPYVdhWq1egvFAiICIiIhXOlVa/OT2XJD8/n4yMDL76ajXt23dyU7SewWSx4N+1O/mHEsn7dYfR4UgJUCIgIiIiFY7mklwen5atsIRHYFv9hdGhSAkwudzYtxMXF8fChQsJCAhgxowZ1KxZs3BfQkICTz/9NLt27WLr1q2XfO7U1Czy8889vs9dwsODym2FDyio8pGSkmF0GCIVRnn+N0X/noiUX3m//0b2xq8Iuf9Bo0NxK6vVTKVKAUaH4VZu6xE4ceIEc+fOJS4ujtGjRzN9+vQi+6+66iomTpyI01m6N/MiIiIicvm8GzUp90lAReG2qkGbNm2iWbNm+Pv7Ex0dzeTJk3E6nYUz6oODg2nRosUFz5Genk56enqRbd7e3uV+Mo6ISEWlRdZEREqP2xKBlJQU6tSpA4DFYiEoKIi0tDTCwsKKfY4FCxYwa9asIttatmzJokWLyn1XjVHCw4OMDkFEyonL/fekPA+X0r+xIuJJ3LqOwJnTD7Kysoqs1Fccw4YNo2/fvkW2eXt7A8bNESjvNKZXpPSU939TLuffE30nIuIpKsIcAbclAhEREezYUVBaKjMzk/T0dEJCQi7pHMHBwQQHB7sjPBERERGRCs1tiUB0dDQzZ87EZrMRHx9Phw4dWLlyJcnJydxzzz3u+lgREREp58rzXBLQfBIpPW5LBMLCwhg5ciT9+/cvLB+6Zs0aDh8+DMCrr77KunXryMrKIjY2lgceeIAePXq4KxwREREpJ7ytlnI7lwQK5pOIlAa3zhG48847ufPOOwvfjxgxovDncePGMW7cOHd+vIiIiIiInIdbEwERkZL07bebmDNnFnl5edSr14DJk58gICCwWG2mTHmEQ4cOFbY7evQw117bkueff7m0L0NERMQjuG1BMRGRkpSamsqzzz7NtGkvsGjRUqpVq87rr88qdptp015g/vwPmD//AyZNepzAwCAmTJhkxKWIiIh4BCUCIlImbNv2HU2aNKVmzSgA+vbtx5o1K4uUKS5OG7vdzn/+82/++c+JREZWKd2LEBER8SBKBESkTEhKSiIiIrLwfXh4BFlZWdhsWZfU5vPPl3HVVeF07Hhz6QQuIiLioZQIiEiZ4HKdewFBs9lySW2WLPmAYcNGnLOdiIhIRaJEQETKhMjIKpw4cbzw/fHjKQQFBePn51fsNnv27MbhcHDdddeXXuAiIiIeSomAiJQJrVu34ddfd5KYeBCATz/9mPbtO15Sm59++pHrr78Bk8lUeoGLiIh4KJUPFZEyoVKlMB577EmmTJlEfr6d6tVrMGXK0+zevYvp06cxf/4H521zWmJiIlWqVDXwKkREPNuVlGkGWLr0Qz7//FNyc3Np1KgJjz76BN7e3kZcihSDEgERKTPato2mbdvoItuCg0OYP/+DC7Y5beJElQsVETmf0yWYX3/9bWrWjOK112by+uuzePjhR4vVZuPGdXz88RJef/1tAgODeOKJSSxZ8gFDhw437qLkgjQ0SERERESuuEzzl1+uYODAuwgODsFsNvPww4/RvXtPQ67FCHFxcfTq1YsBAwaQmJhYZN/JkycZPnw4MTExzJ49u3D7+vXr6d27N3369OGXX34BID4+nhtuuIHY2FhiY2P55JNP3BazegRERERE5IIlmE8P/blQm8TEg6SmnmTChLGcOJHC1Vdfx+jR/yz16zDCiRMnmDt3LsuXL2fbtm1Mnz69yA3/a6+9RteuXRk0aBCDBg2iS5cu1K1bl2eeeYYlS5aQmprKpEmTCm/6b7nlFp577jm3x61EQMRDXek4zV69ulK5ckRh28GDh3LrrT1K9RpERKTsuNIyzfn5+WzbFs/06S/h7e3DtGlPMXfua4wbN9Et8ZaWo0eP4nA4imwLDg4mODi48P2mTZto1qwZ/v7+REdHM3nyZJxOJ2ZzweCbjRs3MnDgQCwWC926dWPDhg1kZmYSFhZGeHg44eHh2O12kpKSAAgJCSmVa9PQIBEPdHoM5rRpL7Bo0VKqVavO66/PKnabgwf3ExgYzPz5HxS+lASIiMiFXGmZ5sqVw+nQ4WYCAgLx8vKiW7ee7Ny5o1SvwR2GDBlCly5dirwWLFhQpE1KSgp16tQBwGKxEBQURFpaWuH+kydPEhVVMJwqMjKS5OTkIsecuR1g8+bNxMbGMmrUqMJt7qBEQMQDXek4zV9+2YHFYmbs2JEMGzaQefPePOtphoiIyJmutExzp06dWb9+Lbm5ObhcLr75ZgNNmjQtzUtwi/fff5+vvvqqyGvYsGFntTvz/+isrKwipapdLlfh/jP3OZ3Os4655pprmD59OkuWLKFq1aq88sorbroyDQ0S8UhXOk7T4XDQqtWNjB49jtzcXB55ZBwBAQH07z+41K9FRETKhist09y3752kp6dz771DcTgcNGzYmEceeczgq7pyVatevOx0REQEO3YU9H5kZmaSnp5eZHhP5cqVOXDgAA0bNiQhIYGIiAgiIiJISEgAChKF09t9fX1p1qwZAHfccQfPPPOMG66qgBIBEQ90peM0b7utb+F7b29vBgwYwkcfLTE0EQip5I+31XLxhmVQXr6DU6k2o8MQEbliV1Km2WKxMGLE/YwYcb/b4/Q00dHRzJw5E5vNRnx8PB06dGDlypUkJydzzz330KlTJ+Lj46lXrx5bt27l2WefpW7duqSlpZGSksKJEyeoVq0aERERfPHFF3Tq1AkfHx/WrVtH8+bN3Ra3EgERDxQZWYVdu3YWvj/fOM3ztfnyyxXUr9+Q+vUbAAVPGiwWY/+6e1stvPz1b4bG4C7jOzQxOgQRETFQWFgYI0eOpH///gQEBDBjxgzWrFnD4cOHARg1ahQTJkxg8eLFxMTE0LhxYwCeeuopRowYgcViYdq0acDphGoEx48fp0GDBkyfPt1tcSsREPFArVu3YdasV0hMPEjNmlHnHad5vjb79v3Jxo3rmDbtBfLz7Xz8cZwmC4uIiLjRnXfeyZ133ln4fsSIEYU/V6pUiXnz5p11TMeOHenYsej/7926daNbt27uC/QMmiws4oHOHIM5ZEg/9u37gwcffIjdu3cxfPjgC7YBGDHifoKCghk2bCDDhg2iRYtr6N27j3EXJCIiIh5HPQIiHupKxmn6+vry2GNPuT1GERERKbvUIyAiIiIiUgGpR0BERKQCuJLVyjMzM5k+fSoHDuzH5XLRvXsMd9013JgLkfNSdTa5VEoEREREyrnTK5G//vrb1KwZxWuvzeT112fx8MOPFqvNW2+9Tnh4JNOmvUB2djZDh/bn2mtb0rz51QZelfydqrPJpdLQIBERkXLuSlcrHzfuYcaMGQfAiRPHsdvzzupNEJGyRz0C4hHUZS0i4j5Xulp5QEAgVquVqVOfYMOGr2jfvhNRUbVK/TpEpGQpERDDlccua43TFBFPcqWrlZ/25JPP8PDDk5ky5RHmz3+Le+8dWbKBikipUiIghjtXd/Tw4YOYOHESJpPpom3GjXsYh8MBeE6XtcZpiognudLVyuPjt1CvXn0qVw7H39+frl27sWHDulK9BhEpeZojIIa7UHd0cdqYTKbCLuu77x7Atddery5rEZEztG7dhl9/3Uli4kGA865Wfr4269at4Z135uJyucjLy2PdujVcf/0NpXsRIlLilAiI4Uqyy/rzz9eSkZHO/PlvlWyQIiJl2JWuVv7gg+PJysrk7rsHcN99Q2nUqAl33jnIwCsSkZKgoUFiOHVZi4i435WsVh4UFMTTTz/n9hhFpHSpR0AMpy5rERERkdKnREAMpy5rERERkdKnoUHiEdRlLSIiIlK61CMgIiIiIlIBKREQEREREamANDRIRETEg5XnlcpBq5WLGEmJgIiIiAcrzyuVg1YrFzGShgaJiIiIiFRASgRERERERCogDQ2SK6KxqyIiIiJlkxIBuSIauyoiIiJSNmlokIiIiIhIBaREQERERESkAlIiICIiIiJSASkREBERERGpgJQIiIiIiIhUQEoEREREREQqICUCIiIiIiIVkBIBEREREZEKSImAiIiIiEgFpERARERERKQCUiIgIiIiIlIBKREQEREREamA3JoIxMXF0atXLwYMGEBiYmKRfSdPnmT48OHExMQwe/Zsd4YhIiIiIuJWl3Pfu379enr37k2fPn345ZdfAHA4HDz11FPExMQwbtw4cnJy3Baz2xKBEydOMHfuXOLi4hg9ejTTp08vsv+1116ja9euLF++nI0bN7J79253hSIiIiIi4jaXc9+bl5fHM888wzvvvMMLL7zAk08+CcC6detITU1lxYoVVK9enSVLlrgtbqu7Trxp0yaaNWuGv78/0dHRTJ48GafTidlckHts3LiRgQMHYrFY6NatGxs2bKBx48ZFzpGenk56enqRbd7e3kRERGCxGDOqqVqQnyGfW1qs1kv/XvWdnFt5/l70nZxN38nZ9J2cTd/Juen/nrPpd+Vsl/udXK7T95pHjx7F4XAU2RccHExwcHDh+8u5783MzCQsLIzw8HDCw8Ox2+0kJSWxYcMGbrzxRgB69OjByy+/zLBhw9xyjW5LBFJSUqhTpw4AFouFoKAg0tLSCAsLAwq6SKKiogCIjIzkxx9/POscCxYsYNasWUW2DRw4kKeffprgYGN+0QdcV9uQzy0tlSoFXPIx+k7OrTx/L/pOzqbv5Gz6Ts6m7+Tc9H/P2fS7crbL/U6u1IQJE866T33wwQcZO3Zs4fvLue8985jT25OTk0lJSaFu3bpFtrmL2xIBAJfLVfhzVlYWJpOpyL7T+/++77Rhw4bRt2/fs7bbbDb8/f3dELGIiIiISIHMzExefPHFs7af2Rtw2uXc9zqdznMec3r7+e6RS4rbEoGIiAh27NgBFHyJ6enphISEFO6vXLkyBw4coGHDhiQkJBAREXHWOf7e7SIiIiIiUloCAwMJDAy8aLvLue+NiIggISEBKEgU/r69Xbt2571HLiluG2wVHR3Nrl27sNlsxMfH06FDB1auXMm8efMA6NSpE/Hx8TgcDrZu3UrHjh3dFYqIiIiIiNtczn1vixYtSEtLIyUlhd9//51q1aoRERFR2BYgPj7erffIbusRCAsLY+TIkfTv35+AgABmzJjBmjVrOHz4MACjRo1iwoQJLF68mJiYmLMmCouIiIiIlAWXe9/71FNPMWLECCwWC9OmTQOgc+fObN68mZiYGOrXr8+ECRPcFrfJdeaAJhERERERqRC0srCIiIiISAWkREBEREREpAJSIiAiIiIiUgEpERARERERqYCUCIiIiIiIVEBKBDxUXFwcvXr1YsCAASQmJhodjkeYO3cu7dq1Y/78+UaH4hEyMzP517/+RWxsLAMGDODgwYNGh2S4w4cP88ADDxATE8Ptt9/OH3/8YXRIHiMpKYnrrruusDZ1Rde0aVNiY2OJjY3lmWeeMTocj/H999/Tt29fYmNjC+ufV2Tz5s0r/D3p3bs3LVq0wGazGR2W4d577z06d+5M9+7d+f77740OR66Ayod6oBMnTjBgwACWL1/Otm3biIuLY/bs2UaHZbg//viDBQsWUK9ePYYPH250OIbbvn07GRkZdOjQgffee4/t27fz0ksvGR2WoZKSksjKyqJu3bq8++677Nq1i+eee87osDzCv/71L37//Xcef/xxbrzxRqPDMVznzp1Zt26d0WF4lNzcXHr37s3bb79N9erVSUhIoF69ekaH5TE+/PBDkpOTGTNmjNGhGCorK4suXbqwdu1ajh07xpQpU1i8eLHRYcllUo+AB9q0aRPNmjXD39+f6Ohotm/fjtPpNDosw9WvX9+ty2yXNddddx0dOnQA4Prrr+fIkSMGR2S8yMhI6taty4kTJ9i/fz/Nmzc3OiSPsH37dmw2G02bNjU6FI8RGhpqdAgeZ/PmzVx33XXUrFkTs9msJOAMubm5vP/++/zjH/8wOhTDeXl5UaVKFfz9/albt67+LpVxSgQ8UEpKCnXq1AHAYrEQFBREWlqasUGJR/v5559p1qyZ0WF4hK1bt9K+fXv279/PwIEDjQ7HcE6nkxdffJFHH33U6FA8SnJyMgMHDmTAgAH8+OOPRofjEQ4fPoyPjw8PPPAAffv2ZevWrUaH5DHWrVtH+/bt8fb2NjoUw3l7ezNw4EBGjx7NnDlzGDRokNEhyRVQIuChzhyxlZWVhclkMjAa8WSZmZnMmzePu+++2+hQPELr1q356aefuPbaa3nxxReNDsdwn3zyCW3atKFmzZpGh+JRXnvtNRYuXMhdd93FI488YnQ4HiE7O5t9+/bx4osvMnXqVM2dOMP69euJjo42OgyPYLPZ+Pbbbxk0aBDff/89O3bsMDokuQJKBDxQREQECQkJQMFNXnp6OiEhIQZHJZ4oLy+PsWPH8sADDxAVFWV0OB7D29ubYcOGsXbtWqNDMdyqVavYsGED/fv3Z8OGDTz99NPs2bPH6LAMd/XVV+Pt7U1MTAxpaWnk5eUZHZLhqlSpQq1atQgKCqJ58+acPHnS6JA8xm+//UaDBg2MDsMjfPXVVzRt2pSOHTsyd+5cVqxYQUZGhtFhyWVSIuCBoqOj2bVrFzabjfj4eDp06IDZrD8qKcrpdPLoo4/SrFkz+vbta3Q4HiEuLo4///wTl8vFqlWrqFatmtEhGW7u3LksXbqUuLg4OnXqxFNPPUXDhg2NDstQW7Zs4dChQwDEx8dTvXp1Dfmg4P+erVu3kpmZyS+//EKVKlWMDsljJCcnU6lSJaPD8AgOh4OffvqJvLw8Tp06xYkTJ7Db7UaHJZfJanQAcrawsDBGjhxJ//79CQgIYMaMGUaHZLikpCTuv/9+jh8/jtlsZv369SxYsMDosAy1aNEiVqxYwTXXXENsbCwAM2fOpFatWgZHZpyrr76ap59+muTkZIKCgpg+fbrRIYkHuuqqq3jyySdJSkrC29ub559/3uiQPEJYWBjjxo1j8ODBOBwOVdw6g9Pp1BDdv/Tq1Ysff/yR7t27YzabeeSRRwgLCzM6LLlMKh8qIiIiIlIBabyJiIiIiEgFpERARERERKQCUiIgIiIiIlIBKREQEREREamAlAiIiIiIiFRASgRERKSIHTt20Lt3b5UuFhEp55QIiIiUAy6Xi8WLF1+03b/+9S+Sk5Mv2CYuLo6RI0cyfvz4kgpPREQ8kBIBEZFyIDs7m0WLFl203YsvvkhERMQF2xw4cIC6detisVhKKjwREfFASgRERDyY3W7nmWeeoV+/fnTr1o2tW7eSk5PDlClT6NWrF4MGDSIpKYlhw4aRkJBAbGwsP/zww3nPFxsby6FDhzh06BDDhg3jySef5NZbb2XChAm4XC7i4uLYuXMn48eP5+uvv+bkyZOMHj2a3r17c++993Ls2LFSvHoREXEnrSwsIuLhfvnlF1q0aMF3333H7NmzadWqFRkZGTz22GMkJSURERHBkSNHGDNmDMuWLbvguWJjY5k9ezYAffv2ZdGiRURFRdGtWzfefvtt6taty9ChQ3nsscdo0qQJDz/8MFdffTV33303X3zxBcuXL+eNN94ojcsWERE3U4+AiIiHCw8P5/XXX+eDDz7gyJEjbNy4kaFDh2IymahSpQpm8+X9U16tWjXq16+Pt7c3DRo04Pjx42e12bx5M7169QKgR48e7NixA4fDcUXXIyIinkGJgIiIBzt06BAjR47k6quv5sknn8TlcuF0OjGZTCX6OVarleJ2EKsjWUSkfFAiICLiwXbu3ElUVBTt2rXj999/B6BNmzZ88MEHuFwujh8/TmZmJldddRWpqakl/rT+pptuYsWKFQCsXLmSZs2aYbVaS/QzRETEGEoEREQ8WJs2bcjIyOD222/n559/JiAggDFjxnDs2DF69erFuHHjSE5Oxs/Pj3bt2tGzZ082btxYYp//2GOPsXnzZnr37s1HH33E1KlTS+zcIiJiLE0WFhERERGpgNS/KyJSzvzyyy9MmTLlrO0LFy4kJCTEgIhERMQTqUdARERERKQC0hwBEREREZEKSImAiIiIiEgFpERARERERKQCUiIgIiIiIlIBKREQEREREamA/g/u7wpBmjBkIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据节点实施分箱  \n",
    "dev_slct2 = combiner.transform(dev_slct1)\n",
    "val2 = combiner.transform(val[dev_slct1.columns])\n",
    "off2 = combiner.transform(off[dev_slct1.columns])\n",
    "# 分箱后通过画图观察  \n",
    "from toad.plot import  bin_plot, badrate_plot  \n",
    "bin_plot(dev_slct2, x='act_info', target='bad_ind')  \n",
    "bin_plot(val2, x='act_info', target='bad_ind')  \n",
    "bin_plot(off2, x='act_info', target='bad_ind') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "warming-civilization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='act_info', ylabel='prop'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adj_bin = {'act_info': [0.16666666666666666,0.35897435897435903,]}  \n",
    "combiner.set_rules(adj_bin)\n",
    "\n",
    "dev_slct3 = combiner.transform(dev_slct1)\n",
    "val3 = combiner.transform(val[dev_slct1.columns])\n",
    "off3 = combiner.transform(off[dev_slct1.columns])\n",
    "\n",
    "# 画出Bivar图\n",
    "bin_plot(dev_slct3, x='act_info', target='bad_ind')  \n",
    "bin_plot(val3, x='act_info', target='bad_ind')  \n",
    "bin_plot(off3, x='act_info', target='bad_ind') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "determined-intensity",
   "metadata": {},
   "source": [
    "## 5 绘制负样本占比关联图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "similar-issue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='samp_type', ylabel='badrate'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([dev_slct3,val3,off3], join='inner')   \n",
    "badrate_plot(data, x='samp_type', target='bad_ind', by='act_info') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "frozen-tenant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='samp_type', ylabel='badrate'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([dev_slct3,val3,off3], join='inner')   \n",
    "badrate_plot(data, x='samp_type', target='bad_ind', by='person_info') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assumed-phase",
   "metadata": {},
   "source": [
    "## 6  WOE编码，并验证IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "corporate-kentucky",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25245\n",
       "1    24339\n",
       "2    15720\n",
       "Name: act_info, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_slct3['act_info'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "established-challenge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.974292\n",
       "1.0    0.025708\n",
       "Name: bad_ind, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_slct3[dev_slct3['act_info']==0]['bad_ind'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cooperative-junction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.987838\n",
       "1.0    0.012162\n",
       "Name: bad_ind, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_slct3[dev_slct3['act_info']==1]['bad_ind'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "expressed-headquarters",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.990585\n",
       "1.0    0.009415\n",
       "Name: bad_ind, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_slct3[dev_slct3['act_info']==2]['bad_ind'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "radio-socket",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = toad.transform.WOETransformer()  \n",
    "dev_slct3_woe = t.fit_transform(dev_slct3, dev_slct3['bad_ind'], \n",
    "                                      exclude=ex_lis) \n",
    "val_woe = t.transform(val3[dev_slct3.columns])  \n",
    "off_woe = t.transform(off3[dev_slct3.columns])  \n",
    "data = pd.concat([dev_slct3_woe, val_woe, off_woe])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "studied-generation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>uid</th>\n",
       "      <th>td_score</th>\n",
       "      <th>jxl_score</th>\n",
       "      <th>mj_score</th>\n",
       "      <th>zzc_score</th>\n",
       "      <th>zcx_score</th>\n",
       "      <th>person_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>act_info</th>\n",
       "      <th>samp_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000002</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>-0.029581</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>-0.306411</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>-0.582769</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000011</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.338661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>0.556362</td>\n",
       "      <td>-0.582769</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A10000481</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>1.283319</td>\n",
       "      <td>0.556362</td>\n",
       "      <td>-0.323991</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000069</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.029581</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>-0.676743</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>-0.582769</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A10001225</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>-0.018649</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>1.047570</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95801</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998107976</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>1.283319</td>\n",
       "      <td>1.047570</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95802</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998176292</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95803</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998322771</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95804</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998973383</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>0.112487</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.240169</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95805</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436392001380983</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>-0.023544</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>1.283319</td>\n",
       "      <td>1.047570</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95806 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bad_ind                     uid  td_score  jxl_score  mj_score  \\\n",
       "0          0.0                A1000002 -0.000706  -0.029581  0.033478   \n",
       "1          0.0                A1000011  0.000176   0.020754 -0.019345   \n",
       "2          0.0               A10000481  0.000176   0.020754 -0.019345   \n",
       "3          0.0                A1000069  0.000176  -0.029581 -0.019345   \n",
       "4          0.0               A10001225 -0.000706   0.020754  0.033478   \n",
       "...        ...                     ...       ...        ...       ...   \n",
       "95801      0.0  Ab99_96436391998107976 -0.000706   0.020754 -0.019345   \n",
       "95802      0.0  Ab99_96436391998176292  0.000176   0.020754  0.033478   \n",
       "95803      0.0  Ab99_96436391998322771  0.000176   0.020754  0.033478   \n",
       "95804      0.0  Ab99_96436391998973383  0.000176   0.020754 -0.019345   \n",
       "95805      0.0  Ab99_96436392001380983  0.000176   0.020754 -0.019345   \n",
       "\n",
       "       zzc_score  zcx_score  person_info  finance_info  credit_info  act_info  \\\n",
       "0       0.018550  -0.052176    -0.306411     -0.607329    -0.822677 -0.582769   \n",
       "1       0.018550  -0.052176     0.338661     -0.607329     0.556362 -0.582769   \n",
       "2       0.018550  -0.052176     0.625661      1.283319     0.556362 -0.323991   \n",
       "3       0.018550  -0.052176    -0.676743     -0.607329    -0.822677 -0.582769   \n",
       "4       0.018550  -0.052176    -0.018649     -0.607329     1.047570  0.438342   \n",
       "...          ...        ...          ...           ...          ...       ...   \n",
       "95801   0.018550  -0.052176     0.625661      1.283319     1.047570  0.438342   \n",
       "95802   0.018550  -0.052176     0.625661     -0.607329    -0.822677  0.438342   \n",
       "95803   0.018550  -0.052176     0.625661     -0.607329    -0.822677  0.438342   \n",
       "95804   0.018550   0.112487     0.625661     -0.607329    -0.240169  0.438342   \n",
       "95805  -0.023544  -0.052176     0.625661      1.283319     1.047570  0.438342   \n",
       "\n",
       "      samp_type  \n",
       "0           dev  \n",
       "1           dev  \n",
       "2           dev  \n",
       "3           dev  \n",
       "4           dev  \n",
       "...         ...  \n",
       "95801       off  \n",
       "95802       off  \n",
       "95803       off  \n",
       "95804       off  \n",
       "95805       off  \n",
       "\n",
       "[95806 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collective-relation",
   "metadata": {},
   "source": [
    "- 计算训练样本与测试样本的PSI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "third-cookbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>psi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uid</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samp_type</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>td_score</td>\n",
       "      <td>8.778656e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zcx_score</td>\n",
       "      <td>4.183912e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jxl_score</td>\n",
       "      <td>2.901553e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>zzc_score</td>\n",
       "      <td>3.764148e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mj_score</td>\n",
       "      <td>5.005908e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bad_ind</td>\n",
       "      <td>4.128345e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>9.489392e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>act_info</td>\n",
       "      <td>1.182993e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>person_info</td>\n",
       "      <td>1.278102e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>finance_info</td>\n",
       "      <td>1.341445e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature           psi\n",
       "0            uid  0.000000e+00\n",
       "1      samp_type  0.000000e+00\n",
       "2       td_score  8.778656e-07\n",
       "3      zcx_score  4.183912e-06\n",
       "4      jxl_score  2.901553e-05\n",
       "5      zzc_score  3.764148e-05\n",
       "6       mj_score  5.005908e-05\n",
       "7        bad_ind  4.128345e-03\n",
       "8    credit_info  9.489392e-02\n",
       "9       act_info  1.182993e-01\n",
       "10   person_info  1.278102e-01\n",
       "11  finance_info  1.341445e-01"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psi_df = toad.metrics.PSI(dev_slct3_woe, val_woe).sort_values(0)  \n",
    "psi_df = psi_df.reset_index()  \n",
    "psi_df = psi_df.rename(columns = {'index': 'feature', 0: 'psi'}) \n",
    "psi_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bored-outline",
   "metadata": {},
   "source": [
    "- 删除PSI大于0.13的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "improved-cooper",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(95806, 11)\n"
     ]
    }
   ],
   "source": [
    "psi_013 = list(psi_df[psi_df.psi<0.13].feature) \n",
    "# 避免不参与计算的几个特征被删掉，把 uid,samp_type,bad_ind添加回来并去重\n",
    "psi_013.extend(ex_lis)\n",
    "psi_013 = list(set(psi_013)) \n",
    "data = data[psi_013]    \n",
    "dev_woe_psi = dev_slct3_woe[psi_013]  \n",
    "val_woe_psi = val_woe[psi_013]  \n",
    "off_woe_psi = off_woe[psi_013]  \n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "employed-engagement",
   "metadata": {},
   "source": [
    "- 卡方分箱后部分变量的IV降低，且整体相关程度增大，需要再次筛选特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "polar-messenger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keep: 7 drop empty: 0 drop iv: 4 drop corr: 0\n"
     ]
    }
   ],
   "source": [
    "dev_woe_psi2, drop_lst = toad.selection.select(dev_woe_psi,\n",
    "                                               dev_woe_psi['bad_ind'],\n",
    "                                               empty=0.6,   \n",
    "                                               iv=0.001, \n",
    "                                               corr=0.5, \n",
    "                                               return_drop=True, \n",
    "                                               exclude=ex_lis)  \n",
    "print(\"keep:\", dev_woe_psi2.shape[1],  \n",
    "      \"drop empty:\", len(drop_lst['empty']),  \n",
    "      \"drop iv:\", len(drop_lst['iv']),  \n",
    "      \"drop corr:\", len(drop_lst['corr'])) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "noble-manor",
   "metadata": {},
   "source": [
    "## 7 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "sought-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(95806, 6)\n"
     ]
    }
   ],
   "source": [
    "dev_woe_psi_stp = toad.selection.stepwise(dev_woe_psi2,  \n",
    "                                                  dev_woe_psi2['bad_ind'],  \n",
    "                                                  exclude=ex_lis,  \n",
    "                                                  direction='both',   \n",
    "                                                  criterion='ks',  \n",
    "                                                  estimator='ols',\n",
    "                                              intercept=False)  \n",
    "val_woe_psi_stp = val_woe_psi[dev_woe_psi_stp.columns]  \n",
    "off_woe_psi_stp = off_woe_psi[dev_woe_psi_stp.columns]  \n",
    "data = pd.concat([dev_woe_psi_stp, val_woe_psi_stp, off_woe_psi_stp]) \n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civilian-contrast",
   "metadata": {},
   "source": [
    "## 8 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "devoted-ivory",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_model(x, y, valx, valy, offx, offy, C):  \n",
    "    model = LogisticRegression(C=C, class_weight='balanced')      \n",
    "    model.fit(x,y)  \n",
    "      \n",
    "    y_pred = model.predict_proba(x)[:,1]  \n",
    "    fpr_dev,tpr_dev,_ = roc_curve(y, y_pred)  \n",
    "    train_ks = abs(fpr_dev - tpr_dev).max()  \n",
    "    print('train_ks : ', train_ks)  \n",
    "\n",
    "    y_pred = model.predict_proba(valx)[:,1]  \n",
    "    fpr_val,tpr_val,_ = roc_curve(valy, y_pred)  \n",
    "    val_ks = abs(fpr_val - tpr_val).max()  \n",
    "    print('val_ks : ', val_ks)  \n",
    "    \n",
    "    y_pred = model.predict_proba(offx)[:,1]  \n",
    "    fpr_off,tpr_off,_ = roc_curve(offy, y_pred)  \n",
    "    off_ks = abs(fpr_off - tpr_off).max()  \n",
    "    print('off_ks : ', off_ks)  \n",
    "      \n",
    "    from matplotlib import pyplot as plt  \n",
    "    plt.plot(fpr_dev, tpr_dev, label='dev')  \n",
    "    plt.plot(fpr_val, tpr_val, label='val')  \n",
    "    plt.plot(fpr_off, tpr_off, label='off')  \n",
    "    plt.plot([0,1], [0,1], 'k--')  \n",
    "    plt.xlabel('False positive rate')  \n",
    "    plt.ylabel('True positive rate')  \n",
    "    plt.title('ROC Curve')  \n",
    "    plt.legend(loc='best')  \n",
    "    plt.show() \n",
    "\n",
    "def xgb_model(x, y, valx, valy, offx, offy):  \n",
    "    model = xgb.XGBClassifier(learning_rate=0.05,  \n",
    "                              n_estimators=400,  \n",
    "                              max_depth=2,  \n",
    "                              class_weight='balanced',  \n",
    "                              min_child_weight=1,  \n",
    "                              subsample=1,   \n",
    "                              nthread=-1,  \n",
    "                              scale_pos_weight=1,  \n",
    "                              random_state=1,  \n",
    "                              n_jobs=-1,  \n",
    "                              reg_lambda=300)  \n",
    "    model.fit(x, y)  \n",
    "      \n",
    "    y_pred = model.predict_proba(x)[:,1]  \n",
    "    fpr_dev,tpr_dev,_ = roc_curve(y, y_pred)  \n",
    "    train_ks = abs(fpr_dev - tpr_dev).max()  \n",
    "    print('train_ks : ', train_ks)  \n",
    "\n",
    "    y_pred = model.predict_proba(valx)[:,1]  \n",
    "    fpr_val,tpr_val,_ = roc_curve(valy, y_pred)  \n",
    "    val_ks = abs(fpr_val - tpr_val).max()  \n",
    "    print('val_ks : ', val_ks)  \n",
    "\n",
    "    y_pred = model.predict_proba(offx)[:,1]  \n",
    "    fpr_off,tpr_off,_ = roc_curve(offy, y_pred)  \n",
    "    off_ks = abs(fpr_off - tpr_off).max()  \n",
    "    print('off_ks : ', off_ks)  \n",
    "\n",
    "    from matplotlib import pyplot as plt  \n",
    "    plt.plot(fpr_dev, tpr_dev, label='dev')\n",
    "    plt.plot(fpr_val, tpr_val, label='val') \n",
    "    plt.plot(fpr_off, tpr_off, label='off')  \n",
    "    plt.plot([0,1], [0,1], 'k--')  \n",
    "    plt.xlabel('False positive rate')  \n",
    "    plt.ylabel('True positive rate')  \n",
    "    plt.title('ROC Curve')  \n",
    "    plt.legend(loc='best')  \n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-picnic",
   "metadata": {},
   "source": [
    "- 定义函数调用模型训练的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "continent-newport",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bi_train(data, dep='bad_ind', exclude=None):  \n",
    "    from sklearn.preprocessing import StandardScaler  \n",
    "    std_scaler = StandardScaler()  \n",
    "    # 变量名  \n",
    "    lis = list(data.columns)  \n",
    "    for i in exclude:  \n",
    "        lis.remove(i)  \n",
    "    data[lis] = std_scaler.fit_transform(data[lis])  \n",
    "    devv = data[(data['samp_type']=='dev')] \n",
    "    vall = data[(data['samp_type']=='val')] \n",
    "    offf = data[(data['samp_type']=='off')]\n",
    "    x, y = devv[lis], devv[dep]\n",
    "    valx, valy = vall[lis], vall[dep]\n",
    "    offx, offy = offf[lis], offf[dep]\n",
    "    # 逻辑回归正向\n",
    "    print(\"逻辑回归正向：\")\n",
    "    lr_model(x, y, valx, valy, offx, offy, 0.1)  \n",
    "    # 逻辑回归反向 \n",
    "    print(\"逻辑回归反向：\")\n",
    "    lr_model(offx, offy, valx, valy, x, y, 0.1) \n",
    "    # XGBoost正向\n",
    "    print(\"XGBoost正向：\")\n",
    "    xgb_model(x, y, valx, valy, offx, offy) \n",
    "    # XGBoost反向\n",
    "    print(\"XGBoost反向：\")\n",
    "    xgb_model(offx, offy, valx, valy, x, y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "significant-dating",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归正向：\n",
      "train_ks :  0.4175617517173731\n",
      "val_ks :  0.3588328912466844\n",
      "off_ks :  0.36930421478753034\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归反向：\n",
      "train_ks :  0.38527996483390414\n",
      "val_ks :  0.37396631393463364\n",
      "off_ks :  0.40858234950836764\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost正向：\n",
      "[10:42:41] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:541: \n",
      "Parameters: { class_weight } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[10:42:41] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vincent/anaconda3/envs/jlab/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.4237042266146137\n",
      "val_ks :  0.3595635995538518\n",
      "off_ks :  0.37518296190183736\n"
     ]
    },
    {
     "data": {
      "image/png": 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MzyQ7r/ApHxsrJT717enT1AWfevY0rm+H0lLPpcwrRGf8zfqUyyTHFZacsVaq8XNqyiONOuPv5Es9a/e76pFJkoTxRiQFx9dhSr6MzM4Ny67jUfp2QCZXVHr7K5MkSWzZ8geLFi3g88+/oVkzfyZNeqGqw7onkRAEoRbL1+q5eqvbJ/JqOhfjM+8Ype9kZ0kLHycCGjnRrKEj7o5WZOqyuJIVy+Wss2yNusLNvEQArBSW+Do2JtyzPc2cmuJpWx/5f+YdkSQJU8YNjDejMKZcw5RyBVNmAjIbZyw7PY3KP7zWdgndLikpkblz57B3bwRBQSGVOrjsQdT+X0YQ6gijycTF2HROnE/kakI2VxNySErPL1ru6WpD/w7eeDhZo1DIaFLfHid7FTfyEriSdZk/bsZyNSqWzILCevsWchVNHHxo5xGKn1NTGtl5oijmrF7SazHeiMIQfxpD3BmkvMJqoTK1PXJXHyxb9kTl3wmZomYcFB/UunVrWLhwPkajkf/9bwZPPPEUCkXNuBoSCUEQajCTJBGflMuJ6GT2nUkgK7ew68feSY+nhyWNmxsxqdPJJQ0JE5eBywWF20bEGEnMS0JvMgDgbOWEr2NjGjt408TBG0+b+sUnAElCykoqSgDGhItgMoDKCqVnIIo2g1F6tURm41wn5++4ceM6gYFBvPXWu3h5NazqcMpEJIRqYu7cOYSGtqFfv4FVHYpQzWXn6zh/NZ2zV9KJvJpGdr4OmUUBTZvIad9URZLuGhezLnINQA8WRhUN7TyxUFjd8TkyZPh5NilKAI6WDiXuUzIZMd44jyHuNIb4M0jZyQDIHRugatkDZcNgFPWaIavFsxWWxGAw8NNP3+Pr24ywsHCee24KSqWyRibDuvfrCUINYzJJXLmZzZkrqZy7kk5sYjaSUoeNQwHufvmo1DHkm3K5AdxIARuVNf0b96SBTT1c1M40sKlX7Jl+aUgGHfroA+jObClMAgoLFJ7NUQb3QdkwCLmdm/kPqcUuXbrInDmziIw8y2OPPUFYWHiNuV9QnFqdEPTRB9Bf3HvX+zKZzPw0eWao/Dujatbxnuu88cZ0evbsTbduPQAYN+4pXnjhJZYu/YKCAi05OTk8//xUunfv8UCxCLVPvlbPmStpnLmcxrkr6eRq9Cjs07FtfAXbRtkY0GECEoHmjs1o6dKc+jYetGzUBF2O7IHPTiWdBn3UbnRntiFpspC7NcayxxSUjUKQKS3KpY01mU6n45tvvmL58qXY2dnzwQeL6NWrT1WH9cBqdUKoar179+Ovv7bQrVsP4uPjKCgo4Pfff2XGjDfx9vbhxIljLFmyUCQEASgcCXzmchpHzidx5nIaRpOErVpFYBMHjO4XOJ93Anu1My2c2+Bu7YaHtRsNbOvd0dXjqLYjJff+yziYNNnoz/2FLnIn6PJReAZi0eo5FA2a18gukIqyY8c2li79ggEDBvPKKzNwdHzwWdaqg1qdEFTNOhZ7Fl9ZtYzCwsJZvPhD8vPz2LFjG7169WHkyNEcPLiP3bt3EBl5Fo1GU+FxCNVXRk4Bp2JSOXUplajYdAxGCQdbCx5p40Vbf3cUtln8ePE3EvOSCG/wMMP8BmKhKP8zdFNuGrrTW9Bf2AtGPUqf1li06o/CvUm576um0mjyiY6+SEhIKH37DqB+fU9CQ1tXdVjlqlYnhKqmUqkICwtn//697Nr1Fx9+uIQpUybQunUbQkPb0KZNO95+e1ZVhylUIkmSiE/O5dSlVE7GpBKbWHg27+6opntrL1r5utKsoSNGjGy5uoO/LkVgb2HH5JBxBLr4l1sc+mt/Y4w7hSkrCVNWElJ+JsgUKP06YBHSD4VT6QrL1RVHjhzinXfeJDs7u6gYXW1LBiASQoXr3bsfixd/iL29A9bW1sTHx/L558uwtLQsmvtAqN30BhMX4zIKrwRiUknPLkAGNPV0YHjXprTydaW+i3VRl8yp5LOsubSJjIJM2tdvyzDfgViryqfmjWTQob+wl4KDPyKztEXmWA+FVyByh/qo/Dogt3Upl/3UFtnZ2Sxa9CHr1v1Go0beLF78ebUqRlfeREKoYMHBrcjNzWXw4GHY2zswYMAQnnrqMWxsbAgMDEar1Ypuo1ooV6PnzOXCrqCzV9Mp0BmxUMkJ9HFmcHhjQpq6Ym9zZ9dPvl7DyZQz/HxhLV629Xmy+QgCnP3KJR5TViK6czvQxxyCgrzCqqJ9pokbxPeQmZnBiBGDSU9P45lnJvDcc1OwsrIyv2ENJuZDqIHEfAilU9lt1hQY+Ds6hUORiUTFZiBJ4GBrQaivK638XAlo5ISF6t/HP7UGLTGZVzmfHs2ljMsk5CUhIdHY3psXQydgeR/3Cv5psyRJGBMuoDv5B6bMhMIuIbkcpU8bVP6dUHi2QCarurmDy1N5/87/FKMD+Prrz+nUqQstWrQst88vD2I+BEGohgxGE5FX0zkUmcipS6noDCbcHdX07+BNqJ8b3vXskMtkaA0FxOXGEZdznbjsG8TlXCc5PwUJCZVcha9jY1q7h9DYoRFNHRujus96P5JRjyH2FLpTf2JMuoTM2hGFV0vk1o6oWvZEbl3y4LO6TpIkNm/exKJFH/L558vw9w/gueemVHVYlUokBEEoI0mSuJqQw6HIRI5GJZGTr8dWraJjcH06BNajaQP7ovsBObpcfrm4jtMp55AovJp1tHSgkZ0X7Txa0djBm6YOPqjKoc5Pwak/iD29GVNBfmExuY5PovLvLLqFSiEh4SbvvTeHAwf2EhwcgqVl3fzOREIQhFJKztRw+FwihyITScrQoFTIaeXnSlhgPVo2cUapuLML5nrOTT479Q0ao5Yejbrg69iYhnZeOFjalXtsBaf+QHd0Dda+bZCahKNo2LLOFJN7UGvW/MrHH3+AySQxffobjBo1usYUoytvIiEIwj3kavQci0riUGQSMTeykAH+jRzp196bNv7uWFsV/0/IJJn46cLvIIPX2k6lgW29CotRd24HuqNrUPq2x2PEy6Sm5ZvfSCiSlJRIUFAr3nrrHTw9vao6nColEoIg/IckSZy9kk7EyRucvVI4YtjT1YbhXZvSvoUHzvbmnzQ5knCC2Jx4xjQfWSHJwJSVhCH+DIbLRzEmXULpHYpV1/G1ftKZ8mAwGPjhh+/w9w8gLKwTzz03BYVCIUZiIxKCIBSRJInIa+ms33eVKzezcbC1oEdbLzoE1qOhu22pDxgag5YNl7fQ2N6bh+qVz+AlSTJhTLyE4epxDHGn/6026lAPyw6Po2rRvU5MPPOgLl68wJw5bxAVdZ5Ro0YTFtYJpVJ8b/8Q30QV+Pbbr9m6dTPDho3gypXLnDlzmnHjJtKzZ80vjlUTSZLEhdgM1u2/Ssz1LFzsLRnbx5+OQfXvui9QGhHx+8nR5/J8yDMPXmSuIA/dub/QX9yHlJsGChUKzxYog3qhbBiM3N79gT6/rigoKGDZsi9ZseIbHBwcWbhwCT169K7qsKodkRCqwNatm/noo09o1MibTp3asWvXwRpdMrcmi47PZN3eK1yMz8TJzpKnejUjPLgBKuX9PaOfq89jZ/xegl0D8ba//8lRJIMOfeROCk79UTiQzKslqoeGo/QORaaq3YOjKsLOndv55puvGDhwCK+8MgMHB8eqDqlaEgmhgn3//XK2b9+CXC6nXbv25OfnkZKSxBtvvIKnpxeSJDFhwlgWLfoMJyfnqg63ztDqDKzde4Udx6/jYGvBEz386NKqASrl/fXBaw1a9lw/yM74vRQYdfRr3LPMn2HKTER/6QDGlKsYk68UVhttGIRlu+EoXMs2EFGA/Pw8oqMv0qpVa/r2HYCnpxchIaFVHVa1VqsTwpGEExxKOHbX+zIZPOj47A712/Fw/Tb3XOfQof3s37+Xb7/9AYVCyaxZr9KuXXtcXd348MMl1K/fgPDwtqxY8dODBSOUmiRJHI1KZvXuGDJyCniktRcjujW9YwRxWWgMGiLiD7I7fh95hnxauPjTv3FPGtqVvjicpM2l4MR69Od3AxJyZ09UTdqi9O2AskHz+4qrrjt06ADvvvsWOTk5RcXoRDIwr1YnhKp24sRxevTojaVl4SV+//6D2LLlzyqOqm7K1eg5HZPKrr9vcDUhm0Yetjw/uCW+Xvc3cjdfryHi+n52xe9HY9DQ0qU5/Rr3KFM3kWTUoz+3g4KTG0GvRRXQBYs2j4rRxA8gOzuLjz76gA0b1uLt7cOSJV/U6mJ05a1CE8KmTZv48ssv0ev1PP3004wePfqO5ZGRkbz11lvo9Xrq16/Phx9+iL29fbnt/+H6bYo9i6+sWkaSZPrPazAaDRW+X+Ff2fk61u29wr7TCZgkCQ9na8b28adTcAPk8rLf8M3X57Mrfj8R1/ejMWgJcm1BP58eNLIv3fPrkk6DMTEaw/VIDNdOIOWmoWgYjOXDI1E4e5Y5HuFfhcXoBpGens6zz07kueemYGlpWdVh1SgVlhCSkpJYtGgRa9euxcLCglGjRvHwww/j6+tbtM7cuXOZOnUqXbp04f333+fbb79l2rRpFRVSpWvduh0rV37L4MGPolAo2bx5I61bt+Xy5ZiqDq3WMxhNbNh7mVVbL1CgM9I1tAEdAuvR5LayEmWRp89nV/w+IuIPoDVqCXFrSV+fR2hoV7qDuCkrCU3EMkzJlwvPDBQqFPX9sej8DEqv6lU4rabRarUAODo68dhjTxAe3pnmzQOrOKqaqcISwsGDB2nfvj2Ojo4A9O7dm61bt/LCCy8UrWMymcjLywNAo9Hg4FC7LpU7duzEpUsXGTduDEajgYcf7sCwYSP57bdfqjq0Wi0uKYelm85zMzWPwMbOjHrED09Xm/v6rFx9Hrvi9rHn+gG0xgJauQXR1+cRvMpyj6Agj/yti5C0OViEDkJRrxmKen6ixtADkiSJTZvW88knH/HZZ8sICGjOhAnPV3VYNVqFJYTk5GTc3NyKXru7u3PmzJk71pkxYwbPPPMM8+bNQ61Ws3r16jLto7gyrsnJcpSleGSwNOuUh/HjJzJ+/MQ73lu//t/7CIcP/13mz5TL5bi5lb0ezv1sU5NIksTmA1f5ZmMk9jYWzHrmIR4KrHdfVwTZ2hw2XdzBtpg9FBh0PNwwlOEt+tHIsfTdOkZtHprLJ8k6+gdSTir1R89G3ahFmWMpq9r+OwNcv36d1157jYiICNq1a0eDBi51ot23q4j2VlhCKG6ahdv/YWq1WmbOnMnKlSsJDg7mu+++47XXXmPp0qWl3kdx8yGYTCaz9wdq+nwIJpOpzLXQa/N8CJIkcfxiCn8cvEZ8ci7BTV14tn9zmnq7lLnNObpcdsTtYe+NQ+iNelq7B9PH55HC8hN6zH6eKSsRQ+wpDLGnMCZGg2RCZmmLVdfx5KobklvBv0Ft/p3/8dtvv/DxxwuQyWDGjDeZMmUiaWl5tb7dt6tx8yF4eHhw/PjxotfJycm4u/87qjI6OhpLS0uCg4MBGDlyJEuWLKmocIRayGgyERWbweZDsVyIy6SeszXj+jenQ8t6yMt4VZBVkMOOuAj23TiMwWSgjUcIfX0eoZ6Nxz23k0zGwpIScYVJQMpKBEDu5IlFSF+UjVohd2+KTF47JqOpDlJSkgkNbc2sWW/ToIEncvHdlpsKSwhhYWF8+umnpKeno1ar2b59O++++27Rcm9vbxITE7ly5QpNmjRh586dBAUFVVQ4Qi1yLTGbQ+eSOBqVRFaeDhsrJWN6+9M5pOxPDmUVZPNXXAT7bxzGYDLSrl4ofby742FTckkIqSAPQ/zZwiuB+DOgywe5AkWD5igDHylMAvZuJW4vlI1er79VjK45HTt2YuLEyaIYXQWp0CuEadOmMWbMGPR6PcOHDyc4OJgJEyYwdepUgoKCmD9/Pi+99BKSJOHi4sK8efPKZd+SJNXavyw1eMbTB5KerSUrT8eO49c5FJmIUiEjuKkrHQI9CG7qUuYRxpkFWfwVG8GBm0cwSibaeYTSx6c77tbFH8hNmQmFCSDuFMbES4VdQVZ2KH1ao2wUgtKrJTILdXk0VbjNhQvnmTNnFhcunOfxx5+iY0dRjK4i1bo5lVNTE7CyssbGpuTHC2vqPQRJksjLy0arzcfVtX6Ztq2pfcuJ6flsOnCNw+cTkSSQAQPCfOj1UENsrO5d/6m4NmdoM9keG8HBhKOYJBMP12tDb+/uuFm73LGeZDIUdgXdSgJSVhIAcmcvlI1aofRuhdytSbXrCqqpv/N/FRQU8PXXn7Ny5bc4OTnz+utv8sgjvYpdt7a0uSxq3D2EquLk5EZGRgq5uZklriOXyzGZal5CAFAqLXByqt3dEZIkcfJSKrv/vs75axmolHJ6tWuIfyMnXO2t8HIv+8jTdG0G22MjOHTzKCYk2tdrS2+f7riq/60fJWlzC+cYiD2F4fpZ0GlArkTRIABly56FXUF2ruXZVKEEO3duZ/nypQwePJT//e817O1r1yPp1VWtSwgKhdLs2XNdPKOoKa7czOaXnZeIuVFYhnpQeGO6hXpib3N/z+ynadLZFrubwwmFDzh0qN+WXt7dcFE7I0kSxsybGP95KijpEkgSMrU9Sp+2KL1bofQKFNVFK0leXi4XL16gdeu29O07gIYNGxEUFFLVYdUptS4hCDVTeraW3/dc4VBkIvY2FjzdN4DwoPr3VV4CIFWTxu/HNhBx9RByZIQ1eIhe3l1xUtlhTIxGG7u1sCvon4lmXBpi0WrAra6gxshk1asrqLY7cGAf7703m9zcXLZu3YWNja1IBlVAJAShSml1BrYeiWPrkThMEvTv4E2/9t6oLe/vr2ZKfhpbY3dyNPFvFDI5nTzb08PjIeySrmLY/zO58WdBrwGFEkWDFiiD+6BsFILc1sX8hwvlLjMzg4UL3+ePPzbQpElT3n//I2xsRDG6qiISglAlUrM07Dpxg72nb5JfYOCh5u4M79IUV8f7e1InOT+Frdd2cSzpZGEicA2mn9Ie+YUzGPdsQHurK0jVpB0K71YoPQORqUThs6qUmZnBsGEDycrKZOLEyYwfPwkLC1HOoyqJhCBUqlyNnj8OXmPnietIErTxd6PXQw1p2uD+bhom5iWx7doujiWdQimTES5zpFNSGnbR25EAycUbi9CBhTeE3XxEV1A1oNVqsbKywtHRiSeeeIrw8C74+wdUdVgCIiEIlejclTS++eM8ORo9nYLrM6hjY5zty37DNluXw4mk0xy9ups4Qw4qk0R4lobOmfnYybNQ1PNDGdQXj9BwMgrEGWd1IUkSGzasZcmSj/jyy28JCGjOuHHPVXVYwm1EQhAqnMFoYu3eK2w9Eoenmw3/GxVKw/t4dPRs6nn2Xj/EhYxLmCQTnlo9AyQb2jr64ejth8KtCTIH96KrAKW9HYinyaqFGzeu8847b3HkyEFat26LtbV1VYckFEMkBKFCJWdq+HrDOa4m5NA11JNR3X3va7rKc6lRfHVmBc6WjnSTHAiJjcErsA8W7YbX2lHptcWvv/7EokUfIpfLeOON2QwfPlLUH6qmzP4qeXl5vP3224wdO5bMzEzeeuutojkMBOFeDp9PZM7yoySla5g8pCVjevvfVzJI1aSz4vwveNnWZ3qeDT1jLuIVMlgkgxoiPT2NNm3a8fvvf/DYY4+LZFCNmb1CeO+993B3dyctLQ1LS0tyc3N56623+OijjyojPqEGKtAZWbUjmv1nEvD1dGDioBa4OpTt6SFJkkjRpHI1K44t13aAJDE6MQdZ4lUsOzyBRVDxZQyEqqfX61mx4huaNw8kPLwzEydORi6Xi+RdA5hNCFFRUcyfP589e/agVqtZuHAhAwYMqIzYhBooLimHrzdGkpiWz4AwbwaHN0ZRxjPCgzePsfHyFnL0uQAoZQqezJRwSruJVY/nUTV5qCJCF8rB+fPnmDNnJtHRF3nyybGEh3dGoSj7VaFQNcwmhP9e3hmNRnHJJ9xFkiR2/X2DX3fFYKNW8sqoVjT3cTa/4X/cyE3g14traWjnSf/6HfC6EYNtzAmsFCrU/V5B2UA8nlgdabVavvrqM77/fjkuLq4sXvwFXbt2r+qwhDIymxDatWvHhx9+iFarZd++faxatYqHHhJnaMK/cjV6vtscxclLqUWzldlbl/1xz5u5iXx+6lusVdaMa9QL1ZZPkAwFqPzCsWgzGLmNUwVEL5SH3bt3sGLFNwwdOoKXXpqOvb19VYck3AezCeGVV15h6dKl2NnZsWjRIjp16sSUKVMqIzahBoiOz+TrjZFk5+kY1d2XHu0almm2MkmSuJBxif03DnM6JRJrpRVTbINQbfsc5Apshr+H3OHes5YJVSM3N5eLF6No06Ydffr0p1EjbwIDxSRXNZnZhLBnzx6mTJlyRxJYv349Q4YMqci4hGrOZJLYdPAaGw9cxc1RzcwxbfCpV/qzwovpMZxKOUtyfioXMi5ho7Ciu8KVDpdjsNXHInP1warreJEMqql9+/bw3nuzycvLY9u23djY2IpkUAuUmBB27dqFwWBgwYIFSJJUNFOXwWBg0aJFIiHUYenZWpZtOs/F+Ew6BHrwZC//UhWj0xv1nEk9T5omnU1Xt6GSK7FSWDKwXgcePrAJpVyOsslDWIQOQOHYoBJaIpRVRkYGH344j82bN9GkiS8ffrhYFKOrRUr8VxwVFcXhw4dJS0vj+++//3cDpZJx48ZVSnBC9XPyUgrL/4zCYJQY1785HYNKN3Pb2dTzrIneSKo2HYCmDo15PuQZ1EortPtWoJcrsXn8Q+TWYiKU6qqwGN0AsrOzRDG6WqrEhPBPN9GqVasYPXp0ZcYkVEN6g5HVuy+z88R1GnnYMmlwS+o5my8/kJyfyu+XNnIu7QL1rN2ZHPIs9azdcbJyRC6TI+m16GMOo2z6kEgG1ZRGo0GtVuPo6MSTT46lU6cu+Pn5V3VYQgUwe50/YsQI/vrrr6LRyUajkbi4OKZNm1bhwQnVQ0JaHl9viCQuOZcebb0Y0dUXlfLejx4XGHVsv7aLHXF7UMqVDPUdQFevjijk/z6TLkkSBcfXgV6LRUCXim6GUEaSJLFu3Ro++aSwGF3z5oE8++zEqg5LqEBmE8K0adOIj48nJSWFFi1acPr0afHYaR1y/EIy3/4ZhUopZ+qwYFr53XtOYUmSOJlylrWX/iCjIJOH6rVmSNN+OFjeecNZ0mspOLIa/fldqJp3Q+7hW5HNEMooPj6Od955k2PHjtC27UPY2tpVdUhCJSjVSOXt27czZ84cnnnmGSRJ4u23366M2IQqJEkSfxy8xrp9V2nqac/zg1uaLVWdkJfEb9EbuJgRg6dtfZ4OfBxfx8Z3fm5BHrrInejOboOCPFRBvbFsP0qUNahGfv75B5Ys+QilUsmsWW8zdOgIMRi1jjCbENzd3VEqlfj4+BAdHU3fvn3RaDSVEZtQRXR6I99tucCR80l0CPTg6b4BqJQllx/QGLRsvvoXEdcPYKmwZGSzIYR7tkd+22Q0Jm0O+rPb0Z3bAXoNikYhWIYORCGuDKqdrKwsHnqoPTNnzsHDo15VhyNUIrMJwdramk2bNhEQEMDq1atp0qQJmZmZlRCaUBWycgv4dO1ZrtzMZliXJvRr713i2bskSRxN/Jv1lzeTo8slrEE7Bjbpg53Fv48hmvIz0Z3Ziv78bjDoUDZug0XoQBSu3pXVJMEMvV7H8uXLaN48kM6duzJhwvOiGF0dZTYhvPXWW6xevZrp06ezZs0annrqKXFDuZaKS8rhk9/PkKvRM+XRlrTxdy9x3ficm6yOXs+VrGt42zdkUvDTeNs3LFpuyk1Dd3oz+gt7wGRE2bR94fgCJ8/KaIpQSufOnWXOnDeIibnEU089TefOXUUxujrMbEL4/fffefXVVwFYvHhxRccjVJHjF5L55s/z2FipeH10G7zrFX8TMU+fzx9XtrHvxmFsVNaMDhhB+/ptirqHTNnJ6E79gT76AACqZh2xCOkvRhxXMxqNhi+//IQff1yJq6sbS5Z8QZcuohhdXWc2IURERPC///2vMmIRqoDeYGTjgWv8eSiWpp72THk0CEdby7vWkySJY0knWXNpI/l6DZ29whjQuCfWqsKxCMaMm+hO/YEh5jDI5aiad8UipB9yW5fKbpJQCrt37+D7779j+PCR/N//vYKdnXiKSChFQvDy8uLZZ5+ldevW2NjYFL3/zDPPVGhgQsWSJIl9ZxL4ecclCvRGOofUZ3RP/2LHFxQYdfwQtZqTyWdobO/N46FDaaC0Q9LmY8xIQHdmK4Yrx0GpQhXUC4vgPsitHSu/UcI95eTkcOHCedq1e5i+fQfg49OYFi1aVnVYQjViNiE4OjoCcOPGjYqORagkOr2RH7Zf5MDZRJp7O9H34UYENnYu9iaiSTKxIvJnzqaeZ3DTvvRo1AXjlePk7voKJFPhSio1FqEDUAX1Qm4lzjSroz17dvHee3PQarVs3boLGxtbkQyEu5hNCPPnz6+MOIRKkqfV88maM8Rcz2JQRx8GdWyMXF7y0yR/XNnOmdRIhvsNolvDcIxJMWgjliJ3b1I4uliuQNkoBJmlTYmfIVSd9PR0Fix4j61bN+Pn14w5c+aKYnRCicyXqHwAmzZt4ssvv0Sv1/P000/fVRPpypUrzJ49m6ysLNzc3Pj4449xcBD1bCpKQloeS9acIT1by6QhLWkXUPJTRFA4e9n22N20r9+Wrl4dkbS5aLZ/gszGBXXv/xNXA9VcRkYGw4b1Jycnl8mTp/LMM+NRqUQxOqFkFTb8MCkpiUWLFvHTTz+xYcMGfv31V2JiYoqWS5LE888/z4QJE9i4cSPNmzdn6dKlFRVOnZeYlseHP59EW2Bg+uOhZpOBJEn8enE91io1Q30HIJPJKDixDkmbg7rnFJEMqjGNJh8AJycnxo4dzy+/rGXixMkiGQhmVVhCOHjwIO3bt8fR0RFra2t69+7N1q1bi5ZHRkZibW1N586dAZg0aZKoqlpBdHojc787it5g4pVRofh5Od5zfb3JwIrzP3M56yqDm/TFRmWNMT0e/fndqJp3Q+HSqHICF8rEZDLxww8/0KdPN6KiIgF4+ulx+Pr6VXFkQk1hNiGYTCa++eYbXnvtNXJzc/n6668xGo1mPzg5ORk3N7ei1+7u7iQlJRW9jouLw9XVlddee42BAwcye/ZsrK3Nl1MWym717hiuJWQzYWAgXu737j/O12v49OQyjiedYmCTPoQ1eAhTXgaabZ8gs7DGsu3QSopaKIu4uFgmTnyaGTNm4O/fHHt70fUqlJ3ZewgLFiwgPT2ds2fPArBv3z5SUlKYNWvWPbf7Z4a1293+FIvBYODo0aP8+OOPBAUFsXjxYt5//33ef//9Ugfv4nL/N8fc3OpGl8f+0zfY9fcNhnRpyiPtfe65rtZQwCcRX3EtJ47/6/AsHRu1w5ifw831H4M2h/qjZ2PlWboJcaqLuvA7f/PNN8yfPx8LCwsWLlzIqFF1r1hgXfid/6si2mw2IRw6dIh169YxdOhQbG1tWb58OYMHDzb7wR4eHhw/frzodXJyMu7u//Zbu7m54e3tTVBQ4TysAwYMYOrUqWUKPi0tF5Pp7sRjjpubHSkpOWXerqY5cTGFrzeeo6mnPWP6tbhnm/UmA1+fWUF0+lXGtXySZuoAkpMyyN8wD1N6Auq+/yPHoj45Neh7qyu/c2JiKu3bd+SNN94iMNC3TrT5dnXld77d/bZZLpfd80TabJeRUqm8o/SthYUFSqX5h5PCwsI4dOgQ6enpaDQatm/fXnS/ACA0NJT09HQuXLgAFM7hHBgYaPZzhdLZcTyez9edpaG7Hf83POSeE9oYTUZWRP5MVHo0owOGE+pemKT153djSrmCVdcJKBs0r6zQBTN0Oh1ffPEJe/fuBmD8+EksWvQZ7u6iPIjwYMwe2Zs1a8aqVaswGo1cuXKFFStWEBAQYPaDPTw8mDZtGmPGjEGv1zN8+HCCg4OZMGECU6dOJSgoiM8//5xZs2ah0WioV68eCxYsKJdG1XXp2VrWRFwmuKkLUx5tec/S1QDrL2/mVMpZhvkNpEODdgCYNNkUHF+HwjMQZRMxIVJ1cebMKebMmcWVKzGMHTuOzp27iWJ0QrmRScV19t8mNzeXefPmERERgclkIjw8nJkzZ+Lk5FRZMZZIdBndzWA0sXTTeU5dSmXexIdxdVADJbc5MS+ZuUc/pkP9djwRMAxJr8V4Mwrd+V0Yr0diPfzdGluhtDb9zhpNPp99toSffvoeD496zJr1NuHhne9arza1ubREm0vPXJeR2SuEpKQk5s2bV+YdC5XvakI2X64/R2qWlkEdfYqSwb1suLwFC7mKgU16Y9LmkL/ubaScVFAosWg7tMYmg9omImIXq1at5LHHnmDq1JextRWjjYXyZzYhPPvss3h5eTFixAj69euHhYUY3FId6fRGvt4YiUmSmDo8mJCm5quMxmRe5UxqJAOb9MZWZY1m62KkvEysek1F6dUSmVL81lUpOzubixfP065de/r06U/jxk0JCBD3coSKY/am8u7du5k4cSIRERF0796dd955p+hGsFB9bNh/leQMDeP6NaeVr6vZxw6NJiO/RW/A0dKB7g07oTu5CWP8GSzDnkDl01okgyq2e/dOhg3rz8svTyUvLxeZTCaSgVDhzCYEuVxOly5dWLx4MStXruTcuXM8+uijlRGbUErXErPZejSOziH1ae7jXKpttsdGcD33JiP8BiGd2ozu+DqUvh1QNe9WwdEK95KWlsqrr05j2rQpODm58PXXy0UxOqHSmO0yMhgM7Nq1i7Vr13LmzBn69evHu+++WxmxCaVgMJr4bvMF7G0seKxb6Sasj82OZ/O1v2jjHkJLnQzNiQ0o/cKw6jK+zg1oqk4Ki9ENIC8vjylTXuLpp8ehUqmqOiyhDjGbEMLDw/Hz82PEiBF88skn4h5CNbP1SBzxybm8MDQIayvzB48Co44V53/G3sKOxxp2Q7thPnKnBlh1GotMXmGlrYR7yMvLxcbGFicnJ559diLh4V1o0qRpVYcl1EFmE8Ivv/yCj49PJYQilFVCWh4bD1ylbYA7rZu5md8AWBvzByn5abwYMg753pUYDQWoe0xBprx72kyhYplMJn777Rc++2wxX331LYGBQYwZ82xVhyXUYSUmhLlz5zJz5swSawt99dVXFRaUYJ5Jkvhu8wUsVQpG92xWqm3Opp5n/43DPNKoMz5XzqJLuIhV1wkonBpUcLTCf127doV33nmLv/8+Tvv2YTg6Vv24HkEoMSF06NABgN69e1daMELp7f77BjE3shjXvzkONua78TK12fwY9RuetvXpZ9EQ3cnFKJt1QtWsYyVEK9zuhx9W8OmnH2NpacXbb89j0KBHxb0boVooMSF0794dgNjYWF566aU7lr333nviSaMqdC0xm98iYghs7ExYy3pm1zeajHx1eCVaYwEvNuyJYcdS5E71ser4ZCVEK/yXRpNPp05deP31t3B1LV1XnyBUhhITwieffEJ2djabN28mNze36H29Xs+uXbvMlr8WKkZqloYlv53BTm3B+P7NS3VmueHyFs4mXeDxeh1x2Po5yBVY9ZiCTCXuG1SGgoICli37kqCgYLp06c748ZPuKBgpCNVFiX8rQ0JCcHR0RC6X4+joWPSnXr16fPrpp5UZo3CL3mDis7Vn0RlMvPRYCA625g/omQVZ7Ll+gK5ebWh1fAdyGyesh84RJSkqyalTfzNq1KN8881XnDz5N4BIBkK1VeIVQpcuXejSpQudO3cmODi4MmMSSvD7nsvEJeXy4rAgPF1tSrXNzri9mCQTHWOikfRa1ANmILdzreBIhfz8PD79dBG//LKKevXq88UXywgL61TVYQnCPZl9yuiLL74odrl4yqhynb2SxvZj8XRv7UmoX+n6nSOuH2B3/H7a6BTYJcRi1X0SCmdxZVAZ9uzZzS+/rGLkyCd48cVpYrSxUCOIp4xqgOSMfJZujMTLzabUo5EvpF/it+gNBKlcGXz5PO5DXkbjJq70KlJWViYXLkTx8MMd6NOnP02b+tGsmX9VhyUIpWb2KaNHH32U3NxcbG1tOXXqFNeuXaN///6VFmBdpykw8MnvhfNZvzAsGAuV+clQJEliw+UtOFnYMfJiDFaNQrFpHoYmNdfstsL92bFjG/Pnv4tOp2Pr1l3Y2NiKZCDUOGbvbn3yySfMnj2bmzdvMnnyZNauXcucOXMqITQB4Ocdl0hMy2fykJa4O5qf3wDgVMo54nKu0yM1F6VcgWXHp8Rz7hUkNTWFV16Zyiuv/B9ubu4sW7ZSdA8JNZbZ0hURERGsWrWKX3/9lf79+zNz5kyGDh1aGbHVeZdvZLH/bAJ92zcqVRXTfL2GHXF72B2/D3cDhGZkoe77MnLb0lVAFcrmn2J0Go2GqVNf5qmnnhHF6IQazWxCAFCr1Rw8eJCRI0cChWMRhIplkiRW/RWNo60FA8N8zK4fn3OTz099Q44+l1Za6JOag03fl1HWK11ZC6H0bi9GN37883Tq1BkfnyZVHZYgPDCzXUZOTk7MmTOHc+fOERYWxsKFC3F3d6+M2Oq0A2cSuJaYw4huvlhZ3Dtvx2bHs+Tk1yjlSv7n0ZVR15Op3/U5kQzKmclk4ueff6B3725ERhbe13nqqadFMhBqDbMJ4YMPPsDd3Z2lS5eiVquRyWR88MEHlRFbnVWgM7J27xV8PR1o38Ljnuvm6vP44vRyrJVqprWeRL3Y88hsXVA0FE8UlaerV6/w7LNP8sEHcwkODsHZ2fwUpYJQ05jtMnJ1dWXw4MEcPXqUqKgohg8fjqurGNhUkbYfiyMrT8eUR4PM3gzeeHkL+QYNU0Mn4iTJybsRiUVIf2QyMRq2vHz//XI++2wxVlZq3n33fQYMGCxu0gu1ktmjxr59+xg2bBg7duxg586dDB8+nB07dlRGbHVSdr6OLUfiaN3MDV8vh3uuezUrlgM3j9LNKxxP2/oYYo6AJKH0C6ukaOuGgoICOnfuxrp1fzJw4BCRDIRay+wVwpIlS/jxxx/x9S0cEHXp0iWmT59Ojx49Kjy4umjbkTgK9EaGdbl3v7RJMvHLxXU4WjrQr3EPJElCf+kgclcfMb/BA9JqtSxd+jnBwaF07dqdceOeE/WHhDrB7N9yvV5flAwA/Pz8MBqNFRpUXaUpMBBx6gZt/d2p73LvWkXHEk9yPfcmQ337Y6W0whB7ElNaLCp/US/nQfz993FGjhzC8uXLOH36JCCK0Ql1h9krBCsrK86ePUtQUBAAZ8+eRa0u3QApoWz2nLqJpsBIn4cb3XM9g8nAn1e308jOk9buIUj6AgoOrkLu5IWqeZdKirZ2ycvLZcmSj1m9+icaNPDkq6+W07696HoT6hazCWH69OlMmjQJb29vJEni2rVrLFmypDJiq1P0BhN/HY8noJEjjevb33Pd/TeOkKbNYJT/UGQyGQUnNyLlpmE16A1k8lINLRH+Y9++Pfz228+MHj2WF174P9Rq66oOSRAqndmjR9u2bfnzzz85ffo0kiQREhKCk5OY/7W8bTkcS0ZOAc/2b37P9a5kxbLu8p/4O/kS4OiLLioC3emtKJt1EuMOyigzM4OoqPN06NCR3r374evrh6+v+A6FustsQjAajfz555/s378fhUJBenq6KF1RzhLS8vjj0DUeau5O4D1KVKRq0vj6zAocLR14JmAEmo3vYUq5iqJeMyzbP1aJEddskiQVFaPT6/VFxehEMhDqOrMJ4b333iMmJobBgwcjSRJr1qwhNjaWadOmVUZ8tZ5Jkvhh20UslAoe71HyASlfn88Xp5djkkxMDnkWq5vRaFOuYtnpaVQBXcSjkKWUkpLM/PnvsmvXX7RoEcjs2XNFMTpBuMVsQjhw4AB//vlnUdGuQYMGMWjQIJEQysm2I3FciMvk6b4BONhYFLuOwWRg6dnvSdWk82KrCXhYu5F/aRUyGydUAZ1FMiilwmJ0Ayko0PLSS9N58smxKJXinosg/KNUtYxuf8xUJpNhb3/vm57/2LRpE/369aNnz56sWrWqxPUiIiKK5l+oS+KScli79wpt/N3oFFy/2HUkSeKnC79zKfMKTzYfgZ9TEyRtLsb4syibPixGJJdCTk4OUPh3+bnnprB69XqefnqcSAaC8B9m/0U0adKEJ554gqFDh6JQKNi8eTNOTk589913ADzzzDPFbpeUlMSiRYtYu3YtFhYWjBo1iocffviOMQ0AqampdbI2kt5gZNmm89haqxjbJ6DEs/xd8fs4kniC/o178lC91oXbXj0OkhGVb4fKDLnGMRqN/PjjSr788hO+/noFLVsGMXr0mKoOSxCqrVKdXvr7+xMZGcmZM2fw8vLCxcWF6OhooqOjS9zm4MGDtG/fHkdHR6ytrenduzdbt269a71Zs2bxwgsv3H8Laqi1e69wIzWPZ/o2x1ZdfA39uJzrrL+8mRDXQPr6/Dsy3BBzCLljfeQu9x6vUJddvhzDkCFDWLhwPqGhbUX9LUEoBbNXCPPnz7+vD05OTsbN7d/J4N3d3Tlz5swd63z//fe0aNGCkJCQ+9qHi8v93wx0c7O7720f1NmYVLYfi6dvmA+PtPcpdh2D0cCCE7/jYGnH/4U/g61l4chlQ3YqOQnROHUeiZN76bru/lGVba5Mn3/+OQsXLsTGxobPPvuMIUPqVv2huvI73060uXxUWCeqJEl3vXf7P8ro6Gi2b9/OihUrSExMvK99pKXlYjLdvR9z3NzsSEnJua99Pqh8rYGPVh3H3VHNoPbeJcbx59W/iM26wXNBY9Fkm9BQuJ7u9E5AQtegdZnaUJVtrmxZWfl0796DBQvex2SyILUOzSVdl37nf4g2l55cLrvniXSFJQQPDw+OHz9e9Do5OfmOiXW2bt1KSkoKw4YNQ6/Xk5yczBNPPMFPP/1UUSFVC6t3XyI9p4A3nmyDpYWi2HVu5Caw9dpO2nq0Itgt8I5l+pjDyN2bILcXkxT9Q6vV8tVXnxESEkq3bo8wfvxzyGQyXFzq3oFCEB5EhT2iEhYWxqFDh0hPT0ej0bB9+3Y6d+5ctHzq1Kls27aNDRs2sHTpUtzd3Wt9Moi5nsXe0wn0bteIpp7Fl7Y2moz8GLUaa6WaEX6D71yWcQNTWpy4mXyb48ePMmLEYFas+IZz5wq7JOtS95AglCezCcFkMvHNN9/w2muvkZuby9dff12qaqceHh5MmzaNMWPGMGTIEAYMGEBwcDATJkzg7Nmz5RJ8TZKRU8CXG87hYm/JoHCfEtfbGbeXuJwbjPR/FFuLOyueGmIOg0yGskm7Co62+svNzWXu3DmMHz8GSTKxdOkKXnxRjI0RhAdhtstowYIFpKenFx3E9+3bR0pKCrNmzTL74QMHDmTgwIF3vLds2bK71vPy8mLXrl2ljbnGMUkSyzZFkl9g4PXRrUucIzkxL4k/r/1FK7cgWrvfOQWmJEnoYw6jaNACubVjJURdve3fv4fff1/NU089zeTJU0UxOkEoB2YTwqFDh1i3bh1Dhw7F1taW5cuXM3jwYHObCbfZd/pm0WjkRh7FPxlgkkz8GLUGS7kFjzUbcvfy5MtIOSmo2tTd7z4jI4OoqHOEhXWid+9++Pn507Spr/kNBUEoFbNdRkql8o4JQiwsLMQIzzIwGE38cTCWpg3sSxyNDBARv5+r2bEMbzYIB8u7k4buzFZQqFD6tKnIcKslSZLYuvVPhg7tx4wZr5Cfn4dMJhPJQBDKmdkje7NmzVi1ahVGo5ErV66wYsUKAgICKiO2WuFoVBJp2VpG92pW4s3O5PxUNl7ZRkuX5rTzCL1ruf7KMQxXj2PRdigyi7o1OVFSUhLz5s1hz57dtGwZzJw572Ftfe/Z5ARBuD9mrxBmzpxJZGQkaWlpPP744+Tl5fHGG29URmw1nkmS2Hw4Di83G0KaupSwjolVF35DKVfweMDQu5KGKTMR7Z7lyN0aY9GqX2WEXW1kZGQwfPhAjhw5xP/+9xorV/4sSlQLQgUye4Vga2vLvHnzKiOWWuf4hWRupubx3KDAEq8O9t84TEzmVUYHjMDR8s5HUSWdBs1fnyBTKFH3mFJnZkPLzs7G3t4eJycnpkyZSlhYJxo18q7qsASh1ivVfAjFKc1TRnWZSZLYeOAaDVxtaBdQ/CCyNE066y5vprlzMzrUb3vHMkmS0O75FlNmAup+05Hb1f5aPEajkZ9++p4vv/yUpUtX0rJlEKNGPVnVYQlCnWG2y8jR0bHoj42NDSdPnqyMuGq8f64OBnX0QS6/++rgn7LWMuBx/2F3XUHoTm/GcPU4lg+NQOnZopKirjoxMdGMHfs4H330Ae3aPYybmxiJLQiVzewVwn8rkT733HM899xzFRZQbWAy/Xt10LaEq4NDCce4kHGJkc0exUV95xzVhuvn0B1bg7LJQ6iC+1ZGyFVq+fKlfPHFp9jZ2fL++x/Ru3c/MdpYEKpAmUtXWFtbk5ycXBGx1BpHo5L+vToo5sCWoc3k90t/4OfYhHDPh+9YZspJQbPzS+SODbDq8mydODBKkkTPnr1Zu3Yzffr0rxNtFoTqyOwVwrvvvlv0D1SSJCIjI2nSpEmFB1ZTGU0mNuy/ipebbbFXB5Ik8fPFtRglI6MDRiC/bcYzyaBDs/0zkEyoe72ITGVVmaFXGo1GwxdfLCE0tA3du/fk2WcniiQgCNWA2YTg5HRnd8Y/cyoLxTt0LomkDA0vDA0q9urgWNJJItMuMMxvIG7W/z6KKkkS2n0rMKXFou79EnKHepUZdqU5duwwb7/9Jtevx2NpaUX37j1FMhCEasJsQoiLi2PBggWVEUuNpykwsHbvZRrXtyPU7+6ngrIKcvgtegNNHLzp6tXxjmX6yJ0YLh3Eos0QlN6tKiniypOTk8OiRQtYu/Y3Gjb0ZtmylbRr97D5DQVBqDRmE8LFixeRJEmcxZXCpgPXyMzVMWVo0F3flyRJ/Bq9Dp1Jf1dXkSHhIgWHfkbRKASL1rXz6uvgwX2sX/87Y8eOY9KkF1Cr69aIa0GoCcwmBFdXV/r3709ISAg2Nv+WDBDjEO6UlJ7PX8fjCQ+uT9MGd8918HfyGU6nnGNI037Us/n33oIpLwPtjs+R2bui7jYRmazCpqiodOnp6URFRdKxYyd69epLs2YBNG4s7j8JQnVVYkLQ6XRYWFgQGhpKaOjd9XWEO205EodMJmNY57sPeDm6XFZHr8fbriHdG3Yqel8yGtDs+BxJX4B1/9eQWdaOGj2SJLFlyx8sWDAXk0li69ZdWFvbiGQgCNVciQlh5MiRrFu37q5xCMLdsnILOHgugfDgBjjYWt61fM2ljWgMWp5sPgKF/N9pMwsOrsKUFINVj8konD0rM+QKk5iYwNy5c9i3bw9BQSHMni2K0QlCTVFiQpCksk9eX1f9dfw6RqNE74ca3rXsdEokx5NOMaBxLxrY/vvkkP7CXvRRu7EI6YeqyUOVGW6FSU9PZ/jwQRgMBl555XUef/xJFIri540WBKH6KTEhFBQUcP78+RITQ2BgYLHv1zWaAgO7T96gtb8bHk53ztqVr8/nl4tr8bJtQC/vbkXvG1Ouoj3wPQrPFli0G1bZIZe77Ows7O0dcHZ25oUXXiI8vDNeXncnR0EQqrcSE0J8fDwvvvhisQlBJpOxc+fOCg2spth/JgFNgYE+Dze6a9maS5vI1ecxOeTZoq4ikyYbzfZPkakdsHrkeWTymnsGbTAY+PHHlXz99ecsXfodQUEhjBo1uqrDEgThPpWYEHx9fVm/fn0lhlLzGE0m/joej6+Xw11PFp1IOsWRxBP09XmEhnaF9wckkxHtzi+RtDlYD56J3Kr46TRrgosXL/D22zM5fz6Srl0fwcOjdg6kE4S6pG4U2K8gf0enkpqlZWR3vzveT9Wk89OFtTS2b0Rfnx5F7xcc/Q3jzSisuo5H4epTydGWn2XLvuTrrz/H3t6BBQsW07NnbzFORRBqgRITQtu2bUtaJFB4033b0TjcHdV3jUr+LXoDAE8HPlHUVaSPOYz+zFZULR5B1Sy80uMtT3K5nD59+vPKKzNwdHQyv4EgCDVCiQlBDDy7t5gbWVy5mc3ons3umO/ges5NzqVFMaBxb1zVzgAY0+PR7l2OwsMPyw6PV1XI902jyeezz5bQpk1bUYxOEGqx2jMstpJtOxqPjZWS8KD6d7y/PXY3VgpLunh1AEAqyCu8iWxhjVXPKcgUNauX7vDhgwwbNpBVq1Zy4UIUgEgGglBL1ayjUzWRnJHPyegU+nXwxtLi36eEkvNT+Dv5DD0adcFaZY0kmdDs+hopNw3rATOQWztWXdBllJ2dzccff8D69b/TqJE3y5f/SOvWohtREGozkRDuw1/HryOXy+je2uvO92P3oJAr6HarPIU+KgJj/Bksw8egqOdX3EdVW4cO7WfTpvU8++wEJk6cgpVV7ZybQRCEf4mEUEYmk8SxqCRCm7nhZPdvmYoMbSZHEk/QscFDOFjaIUkmdGe3IXdvgqp5t3t8YvWRlpZKVNR5wsM706tXXwICmuPt3biqwxIEoZKIewhldPlmFtn5eto0c7vj/V3x+5CQ6NGoCwDG+DNIWUlYtOxV7fvcJUnijz82MHRof954Yzr5+XnIZDKRDAShjhEJoYyOXUhGIZcR3PTf2c5ydXnsv3GYdh6huNx6skh39i9kNk4om1TvfveEhJu88MJEZs16DR+fJqxY8ZMoRicIdZToMiqDAp2Rg2cTad3MDbXlv19dxPX96Ex6enp3BcCYFofxRiQW7YYjk1ffrzg9PZ0RIwZhNJp47bWZjBw5GrlcnCMIQl1VoUerTZs28eWXX6LX63n66acZPfrOOjc7duzg008/RZIkvLy8mD9/Pg4Od08uU10cPp9IfoGBR9r8ezNZa9AScf0gIW4tqW/jgSRJFBz6GSxtsGhRPe8dZGZm4OjohLOzM//3f68QFhaOp6eX+Q0FQajVKux0MCkpiUWLFvHTTz+xYcMGfv31V2JiYoqW5+bmMmfOHJYuXcrGjRvx9/fn008/rahwHpgkSew8cZ2G7rb4ef2btPbdOIzGoKH3rWqmhmt/Y7wZhWXbR6vdhDcGg4Hly5fSp093zpw5BcCIEaNEMhAEAajAhHDw4EHat2+Po6Mj1tbW9O7dm61btxYt1+v1zJkzBw8PDwD8/f1JSEioqHAeWHR8JtdT8nikjVfRTWK9Uc/O+L0EOPnhbd8Qyain4PAvyJ08q92TRefOneOppx7jk08+pmPHcOrXb1DVIQmCUM1UWEJITk7Gze3fJ3Hc3d1JSkoqeu3k5ESPHoWF37RaLUuXLi16XR3t/PsGNlZKHm7hUfTeoYTj5Ohy6e1TePDXnd2OlJOCZYcnqlVZ66VLv6Bfv34kJyezcOESPvroU9zc3M1vKAhCnVJh9xBKmkfhv3Jycpg8eTIBAQE8+uijZdqHi4vtfcfn5lb60tNpWRr+jk5hcOemeDVwBMBoMrLryF78XBoT5tcKY14m8ac2Ye3Xjnqh7e87rorg6GjLsGHDeOutt3ByqlvF6MryO9cWos11Q0W0ucISgoeHB8ePHy96nZycjLv7nWelycnJjBs3jvbt2/PGG2+UeR9pabmYTGWf6tPNzY6UlJxSr79u7xUkk8TDAW5F2x1N/JuUvDSGNR1IamoumoiVSAY9stbDyvTZFSE/P49PP11MmzZt6dGjNyNGPIW7uz0pKTlVHltlKuvvXBuINtcN99tmuVx2zxPpCusyCgsL49ChQ6Snp6PRaNi+fTudO3cuWm40Gpk0aRJ9+/Zl5syZ1Xbwlt5gYs+pG4T4uuLuqAbAJJnYFrubBjb1CHQJwJhyDUP0flQteyF3qNqJYg4e3M+wYQP55ZcfuXQpGhDF6ARBKJ0KvUKYNm0aY8aMQa/XM3z4cIKDg5kwYQJTp04lMTGR8+fPYzQa2bZtGwAtW7Zk7ty5FRXSfTl+MZnsfD3d23gWvXc29TyJeUk83eJxZMjQHlyFTG2HZetBVRZnVlYmH330ARs3rsPHpzHLl68iNLR1lcUjCELNU6HjEAYOHMjAgQPveG/ZsmUABAUFceHChYrcfbnYdeI6Hs7WtPApHIEsSRLbYnfjauVMa/dgDJePYEy6hGXnZ5BZqKsszsOHD/HnnxsZP34SEyY8j6WlpfmNBEEQblN9h9FWA1cTsrl8M5vHe/ghv9XtcjEjhtjseB73H4rcZEBzZDVyF29UzTpVenypqSmcP3+Ozp270atXH5o3b0GjRt6VHocgCLWDSAj3sOvEdSwtFHdMgrMtdjcOFnY8XL8tupN/IuWlY9X9OWSVWPJBkiQ2blzHwoXvI5fL2LJlF9bWNiIZCILwQEThmhJk5+s4EpVMWMt6RXWLrmbFEZ0RQ/dGnVHkZ6E79SfKJu1Q1vevtLhu3LjO88+PY/bsN/D19WPlyp9FMTpBEMqFuEIowb7TNzEYTXdMgrM9djfWSjXhDdpTsPc7wITlw49VWkzp6Wk89thgJEni9dffYsSIUaIYnSAI5UYkhGIYjCZ2n7xBc28nPF0Lz75v5iZyJjWSfo17okqLJz/mMBahA5HbuZn5tAeXkZGBk5MTzs4uTJs2nbCwTjRo4Gl+Q0EQhDIQp5fFOHExhfTsAnq2a1j03vbY3VgoLOji2aHwMVNrRyxa9a/QOPR6Pd988xV9+3bj9OmTAAwfPkokA0EQKoS4QvgPSZLYfiweD2froklwUjVpnEg+TTevcCyvnUabchWrrhOQqSpunuGoqEhmz55JdPQFevbsg5dXQ/MbCYIgPACREP7j8o1sriZk82SvZkWPmv4Vtwc5MrrVf4iC9fOQuzdB6dehwmL48stP+eabr3Bycubjjz+le/eeFbYvQRCEf4iE8B+HzidioZLTsWXho6ZZBdkcvnmM9vXbYn1+L7r8TNS9XkQmq7jeNisrNQMHDuHll1/F3r76ThgkCELtIhLCbSRJ4nRMKoE+zlhaFJav3hm/F6Nk4hGXYHQHPkDp2wGFe9Ny3W9eXi5LlnxMu3YP0bNnH55+epyoPyQIQqUTCeE28cm5pGcXMKhjYwDy9Pnsv3GYNh4h2J3ahkEmL/fHTPfv38t7780mKSkRV1dXQBSjEwShaoiEcJvTl9MAim4m77l+gAKjjkfUPhiubsei7VDkNuUzn0BmZgYLF77PH39soEkTX1au/Jng4Fbl8tmCIAj3QySE25yOSaVxfTscbS3RGgqIiD9AkEtzXE5uQbJ1wSK4T7nt6+jRI2zd+icTJ05m/PhJWFhYlNtnC4Ig3A+REG7JytNx9WY2g8MLu4sO3jxCniGf7jhiSovH6pHJyJQPdtBOSUnm/PlzdOnSnZ49e9OiRaB4nFQQhGpDJIRbzlxORQJCfF3RmwzsiNtLMwcfGpzehbxeM5RN2t33Z0uSxIYNa1m48H2USgVbtuxCrbYWyUAQhGpFjFS+5UxMGk52ljTysOVowgmydNl00yqRtLlYho2+7xu9N25cZ9KkccyZMxN/f39WrvwFtdq6nKMXBEF4cOIKgcJpMs9dS6dDCw9MkontcRE0svbA59wRVP6dULjeX1np9PR0RowYhEwmY+bMOQwb9pgoRicIQrUlEgJwMS6DAp2REF9XTqacJVWTxhi9EzKFCot2w8r8eenp6Tg7O+Ps7Mz//jeDjh07Ua9effMbCoIgVCFxugqcjknDQiknoJEj267twsPCgYDYi1i2HoTcuvQjhfV6HUuXfkHfvt04depvAIYNe0wkA0EQaoQ6f4UgSRKnYlJp4eNMdHY0N/MSGZkjR2Hvjqpl6WsInTt3lrffnsmlS9H06dOPRo18Ki5oQRCEClDnE8KN1DzSsrX069CIbdc24Cy3IjgpDsteU5EpVKX6jC+++IRvvvkKFxdXFi/+gq5du1dw1IIgCOWvzieE0zGpADi453L1YiyDMwqw8AxE6R1a6s+wsbFhyJBhTJv2KnZ2dhUVqiAIQoUSCSEmDe96dhxM2Y8dStpmpGA59PF7Pmaam5vLkiULadeuPb169WHMmGdF/SFBEGq8Op0QsvN1XL6RRecO1hxNj6ZvWh7WAd1QOHuVuM3evRHMnTuHlJRk3N09AFGMThCE2qFOJ4Szl9OQgAzrSNTZMh7WgGXbocWum5GRwYcfzmPz5k00berHwoVLCAoKqdyABUEQKlCdTginY1Kxdy4gJuci3TLycGg9BJmVbbHrHjt2hO3btzJp0guMGzcRlUoUoxMEoXapswnBYDRx7mo6bkFxZEnQSbJH1aLbHeskJSVx/vw5unV7hJ49exMY2BJPz5K7kwRBEGqyOpsQLsZnUkAu6fIrdMjKx7n9RGTywq/DZDKxdu1vLF78IUqlkvbtO6BWW4tkIAhCrVZnRyqfvpSKjWcMMkx0tfZB2TAYgLi4WCZOfJr33ptN8+aB/PDDalGMThCEOqFOXiFIksTJq9ehyQ1Ccwqo13U0AOnpaYwc+SgKhZy33nqXRx8dLp4gEgShzqiTCSE+KQeTzWlMMokebqFkGFW4AM7OLrz66huEhXXCw8OjqsMUBEGoVBXaZbRp0yb69etHz549WbVq1V3Lo6KiGDZsGL1792bmzJkYDIaKDKfIgbPXMLon0CJHzx8nM+jbt3tRMbpHHx0ukoEgCHVShSWEpKQkFi1axE8//cSGDRv49ddfiYmJuWOd6dOn8+abb7Jt2zYkSWL16tUVFc4dTl/bSPr1DLYsO8nX3yylR4/eeHs3rpR9C4IgVFcVlhAOHjxI+/btcXR0xNramt69e7N169ai5Tdu3ECr1dKqVSsAhg4desfyipKWlcm+vTs4+8k+dAYZn3zyFfPmfYiTk1OF71sQBKE6q7B7CMnJybi5uRW9dnd358yZMyUud3NzIykpqUz7cHEpfhDZvcQmnEdhraLbI5354rOldaoYnZtb3WnrP0Sb6wbR5vJRYQlBkqS73rv9iR1zy0sjLS0Xk+nuz7kX7/ot2PLxevLzTWi1oNXmlGn7msrNzY6UlLrR1n+INtcNos2lJ5fL7nkiXWFdRh4eHqSmpha9Tk5Oxt3dvcTlKSkpdyyvSDY2NpWyH0EQhJqkwhJCWFgYhw4dIj09HY1Gw/bt2+ncuXPRck9PTywtLTlx4gQA69evv2O5IAiCULkq9Aph2rRpjBkzhiFDhjBgwACCg4OZMGECZ8+eBWDhwoXMnz+fvn37otFoGDNmTEWFIwiCIJghk4rrzK8h7uceAog+x7pCtLluEG0uvSq7hyAIgiDULCIhCIIgCIBICIIgCMItNbq4nVx+/5VIH2Tbmkq0uW4Qba4b7qfN5rap0TeVBUEQhPIjuowEQRAEQCQEQRAE4RaREARBEARAJARBEAThFpEQBEEQBEAkBEEQBOEWkRAEQRAEQCQEQRAE4RaREARBEASglieETZs20a9fP3r27MmqVavuWh4VFcWwYcPo3bs3M2fOxGAwVEGU5ctcm3fs2MHgwYMZNGgQkydPJisrqwqiLF/m2vyPiIgIunfvXomRVRxzbb5y5QpPPfUUgwYNYty4cXXid46MjGTYsGEMGjSI5557juzs7CqIsnzl5uYyYMAArl+/fteyCjl+SbVUYmKi1K1bNykjI0PKy8uTBg4cKF26dOmOdfr37y+dPHlSkiRJev3116VVq1ZVQaTlx1ybc3JypI4dO0qJiYmSJEnS4sWLpXfffbeqwi0XpfmdJUmSUlJSpD59+kjdunWrgijLl7k2m0wmqVevXtKePXskSZKkDz/8UFqwYEFVhVsuSvM7P/7441JERIQkSZI0f/586eOPP66KUMvNqVOnpAEDBkiBgYFSfHz8Xcsr4vhVa68QDh48SPv27XF0dMTa2prevXuzdevWouU3btxAq9XSqlUrAIYOHXrH8prIXJv1ej1z5szBw8MDAH9/fxISEqoq3HJhrs3/mDVrFi+88EIVRFj+zLU5MjISa2vroilpJ02axOjRo6sq3HJRmt/ZZDKRl5cHgEajwcrKqipCLTerV69m9uzZxc41X1HHr1qbEJKTk3Fzcyt67e7uTlJSUonL3dzc7lheE5lrs5OTEz169ABAq9WydOnSotc1lbk2A3z//fe0aNGCkJCQyg6vQphrc1xcHK6urrz22msMHDiQ2bNnY21tXRWhlpvS/M4zZsxg5syZhIeHc/DgQUaNGlXZYZaruXPn0rZt22KXVdTxq9YmBKmYIq4ymazUy2ui0rYpJyeHCRMmEBAQwKOPPloZoVUYc22Ojo5m+/btTJ48uTLDqlDm2mwwGDh69ChPPvkkmzZtomHDhrz//vuVGWK5M9dmrVbLzJkzWblyJfv37+eJJ57gtddeq8wQK1VFHb9qbULw8PAgNTW16HVycvIdl17/XZ6SklLspVlNYq7N/7z3xBNPEBAQwNy5cys7xHJnrs1bt24lJSWFYcOGMXHixKL212Tm2uzm5oa3tzdBQUEADBgwgDNnzlR6nOXJXJujo6OxtLQkODgYgJEjR3L06NFKj7OyVNTxq9YmhLCwMA4dOkR6ejoajYbt27cX9akCeHp6YmlpyYkTJwBYv379HctrInNtNhqNTJo0ib59+zJz5swaf0UE5ts8depUtm3bxoYNG1i6dCnu7u789NNPVRjxgzPX5tDQUNLT07lw4QIAu3btIjAwsKrCLRfm2uzt7U1iYiJXrlwBYOfOnUUJsTaqsOPXA9+WrsY2btwo9e/fX+rVq5e0dOlSSZIkafz48dKZM2ckSZKkqKgoadiwYVKfPn2kl19+WSooKKjKcMvFvdq8fft2yd/fXxo0aFDRnzfeeKOKI35w5n7nf8THx9eKp4wkyXybT506JQ0bNkzq16+f9Oyzz0qpqalVGW65MNfmiIgIaeDAgdKAAQOksWPHSnFxcVUZbrnp1q1b0VNGFX38EjOmCYIgCEAt7jISBEEQykYkBEEQBAEQCUEQBEG4RSQEQRAEARAJQRAEQbhFJAShWvL392fgwIEMHjy46M/MmTPvuc3atWt57rnnKinC0tu5cyfvvfceUFhxdcmSJXe9X5k+++wzduzYUen7Fao/ZVUHIAglWblyJc7OzlUdxgN75JFHeOSRRwA4e/ZsUSnq29+vTEeOHMHX17fS9ytUfyIhCDXOmjVr+PXXX9Hr9WRlZTFhwoS7ylFs376dL7/8EplMhkKh4NVXX6Vdu3bk5OQwd+5coqOj0ev1dOjQgVdffRWl8s5/CjNmzEAmk3H58mXS09Pp2LEjs2bNQqVScfz4cRYsWIBGo0GlUvHSSy/RuXNnUlJSeO2118jIyACgS5cuvPTSS6xdu5Zt27YxefJkfvnlF4xGI3Z2dnh7e7Nt2zZmzJjBqFGj2LdvHxYWFhiNRrp168by5cvx8PAodbyZmZnEx8fTtWtXhg8fzjvvvEN+fj7JyckEBASwePFi1qxZw7lz51iwYAEKhYIuXbqwcOFCjh07htFopEWLFsyaNQtbW9uK/RGFakl0GQnV1tixY+/oMkpLSyMvL4/ffvuNpUuXsn79ehYtWsSHH35417YLFixg9uzZrF27lv/7v//jyJEjAMybN4/AwEDWrl3L+vXrycjI4Lvvvit2/xcuXOC7775j8+bNXL58mV9//ZWMjAymTp3KzJkz2bRpEx988AHTp08nPj6e1atX4+Xlxbp161i1ahWxsbHk5OQUfV5ISAijRo2iX79+TJs2rej9xo0b4+fnx65duwDYv38/np6e+Pr6lilerVbLn3/+yfTp01m9ejVDhgzh119/Zfv27Vy/fp2IiAhGjx5Ny5YtefXVV+nZsydLly5FoVCwdu1aNm7ciLu7OwsXLrzv30yo2cQVglBtldRl9NVXX7Fnzx6uXbvGhQsXyM/Pv2ud/v3788ILL9ClSxc6duzIhAkTgMI+/LNnz7JmzRqg8CBakkcffRQbGxsABg8ezM6dO2nYsCGNGjUqKqXt5+dH69atOXr0KJ06dWLixIkkJCQQFhbG//73P+zs7ErV1hEjRrBu3Tr69OnD2rVrGTFiRJnjbdOmTdH/T58+nQMHDrBs2TKuXbtGcnJysd9TREQEOTk5HDx4ECicM8PFxaVUMQu1j0gIQo2SmJjIyJEjeeyxx2jTpg19+vRh9+7dd603bdo0hg8fzv79+1m7di1Lly5l7dq1mEwmlixZQtOmTQHIzs4uscifQqEo+n9JkpDL5ZhMprvWkyQJg8FAcHAwO3fu5NChQxw+fJgRI0bw+eefl6pdffr0Yf78+Vy+fJljx44VlasuS7y3z3nw8ssvYzQa6du3L127diUhIaHYkskmk4k33niDLl26AJCXl0dBQUGpYhZqH9FlJNQo586dw9nZmcmTJ9OpU6eiZGA0GovWMRgMdO/enfz8fB5//HFmz57N5cuXMRgMhIeHs2LFCiRJQqfT8fzzz/Pjjz8Wu68tW7ag0+koKChg3bp1dOvWjZCQEK5evVpUTvrSpUscO3aMhx56iIULF/LFF1/Qo0cPZs6cia+vL9euXbvjMxUKRbFz31paWtK/f39mzJhBr169UKvVAGWK93b79+9nypQp9OvXD5lMxunTp4u+o9tjCA8PZ9WqVeh0OkwmE2+++SYff/yx2c8XaidxhSDUKB07dmTNmjX06dMHtVpNcHAwzs7OxMbGFq2jVCp54403eOWVV1AqlchkMubNm4eFhQUzZ85k7ty5DBw4EL1eT1hYGOPHjy92X1ZWVjzxxBNkZ2fTu3dvhg0bhlwuZ8mSJbz77rtotVpkMhnz58+ncePGjB07lhkzZjBgwAAsLCzw9/dnwIAB/PHHH0Wf2aFDB1588UVUKtVdJalHjBjBjz/+yJw5c4reK0u8t5s2bRpTpkzBwcEBtVpNu3btiIuLA6Bbt2588MEH6PV6Jk+ezAcffMCjjz6K0WikefPmzJgxoyw/iVCLiGqnglCMGTNm4Ofnx7hx46o6FEGoNKLLSBAEQQDEFYIgCIJwi7hCEARBEACREARBEIRbREIQBEEQAJEQBEEQhFtEQhAEQRAAkRAEQRCEW/4fISLIJg1hCw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost反向：\n",
      "[10:42:43] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:541: \n",
      "Parameters: { class_weight } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[10:42:43] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vincent/anaconda3/envs/jlab/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.3938834608675862\n",
      "val_ks :  0.37747626322745126\n",
      "off_ks :  0.39338531134694127\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bi_train(data, dep='bad_ind', exclude=ex_lis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focal-blank",
   "metadata": {},
   "source": [
    "## 9 计算指标评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "perfect-visit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep = 'bad_ind'  \n",
    "lis = list(data.columns)  \n",
    "for i in ex_lis:  \n",
    "    lis.remove(i)\n",
    "devv = data[data['samp_type']=='dev']\n",
    "vall = data[data['samp_type']=='val']\n",
    "offf = data[data['samp_type']=='off' ]\n",
    "x, y = devv[lis], devv[dep]  \n",
    "valx, valy = vall[lis], vall[dep]\n",
    "offx, offy = offf[lis], offf[dep]\n",
    "lr = LogisticRegression()  \n",
    "lr.fit(x, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "material-orchestra",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集\n",
      "F1: 0.029351208260445533\n",
      "KS: 0.4175617517173731\n",
      "AUC: 0.7721143736676812\n",
      "验证集\n",
      "F1: 0.03797468354430379\n",
      "KS: 0.3588328912466844\n",
      "AUC: 0.7222256797668032\n",
      "跨时间\n",
      "F1: 0.022392178506662464\n",
      "KS: 0.3696948842371405\n",
      "AUC: 0.7436315034285385\n",
      "模型PSI: 0.33682698167332736\n",
      "特征PSI: \n",
      " credit_info    0.098585\n",
      "act_info       0.122049\n",
      "person_info    0.127833\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from toad.metrics import KS, F1, AUC \n",
    "\n",
    "prob_dev = lr.predict_proba(x)[:,1]  \n",
    "print('训练集')  \n",
    "print('F1:', F1(prob_dev,y))  \n",
    "print('KS:', KS(prob_dev,y))  \n",
    "print('AUC:', AUC(prob_dev,y)) \n",
    "\n",
    "prob_val = lr.predict_proba(valx)[:,1]  \n",
    "print('验证集')\n",
    "print('F1:', F1(prob_val,valy))  \n",
    "print('KS:', KS(prob_val,valy))  \n",
    "print('AUC:', AUC(prob_val,valy)) \n",
    "\n",
    "prob_off = lr.predict_proba(offx)[:,1]  \n",
    "print('跨时间')  \n",
    "print('F1:', F1(prob_off,offy))  \n",
    "print('KS:', KS(prob_off,offy))  \n",
    "print('AUC:', AUC(prob_off,offy))\n",
    "\n",
    "print('模型PSI:',toad.metrics.PSI(prob_dev,prob_off))  \n",
    "print('特征PSI:','\\n',toad.metrics.PSI(x,offx).sort_values(0))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worth-consultancy",
   "metadata": {},
   "source": [
    "- 生成模型时间外样本的KS报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "productive-mozambique",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>bads</th>\n",
       "      <th>goods</th>\n",
       "      <th>total</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>bad_prop</th>\n",
       "      <th>good_prop</th>\n",
       "      <th>...</th>\n",
       "      <th>cum_bad_rate</th>\n",
       "      <th>cum_bad_rate_rev</th>\n",
       "      <th>cum_bads_prop</th>\n",
       "      <th>cum_bads_prop_rev</th>\n",
       "      <th>cum_goods_prop</th>\n",
       "      <th>cum_goods_prop_rev</th>\n",
       "      <th>cum_total_prop</th>\n",
       "      <th>cum_total_prop_rev</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.003195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>922.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>0.002165</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.058925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002165</td>\n",
       "      <td>0.020532</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057840</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052827</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003623</td>\n",
       "      <td>0.004079</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.999051</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.067297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.021660</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.993902</td>\n",
       "      <td>0.126222</td>\n",
       "      <td>0.941075</td>\n",
       "      <td>0.123818</td>\n",
       "      <td>0.942160</td>\n",
       "      <td>0.117076</td>\n",
       "      <td>1.054919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004275</td>\n",
       "      <td>0.006260</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.997849</td>\n",
       "      <td>0.002155</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.088963</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001779</td>\n",
       "      <td>0.023219</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.990854</td>\n",
       "      <td>0.215185</td>\n",
       "      <td>0.873778</td>\n",
       "      <td>0.211142</td>\n",
       "      <td>0.876182</td>\n",
       "      <td>0.196892</td>\n",
       "      <td>1.130877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.006621</td>\n",
       "      <td>0.008334</td>\n",
       "      <td>6.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>0.006881</td>\n",
       "      <td>0.993119</td>\n",
       "      <td>0.006928</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.055346</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.025551</td>\n",
       "      <td>0.036585</td>\n",
       "      <td>0.981707</td>\n",
       "      <td>0.270531</td>\n",
       "      <td>0.784815</td>\n",
       "      <td>0.265728</td>\n",
       "      <td>0.788858</td>\n",
       "      <td>0.233946</td>\n",
       "      <td>1.244467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008360</td>\n",
       "      <td>0.008793</td>\n",
       "      <td>11.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>0.012415</td>\n",
       "      <td>0.987585</td>\n",
       "      <td>0.012571</td>\n",
       "      <td>0.033537</td>\n",
       "      <td>0.055921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004483</td>\n",
       "      <td>0.026939</td>\n",
       "      <td>0.070122</td>\n",
       "      <td>0.963415</td>\n",
       "      <td>0.326452</td>\n",
       "      <td>0.729469</td>\n",
       "      <td>0.321189</td>\n",
       "      <td>0.734272</td>\n",
       "      <td>0.256330</td>\n",
       "      <td>1.312067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.008883</td>\n",
       "      <td>0.010726</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>0.005771</td>\n",
       "      <td>0.994229</td>\n",
       "      <td>0.005804</td>\n",
       "      <td>0.021341</td>\n",
       "      <td>0.077075</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004729</td>\n",
       "      <td>0.028126</td>\n",
       "      <td>0.091463</td>\n",
       "      <td>0.929878</td>\n",
       "      <td>0.403528</td>\n",
       "      <td>0.673548</td>\n",
       "      <td>0.397121</td>\n",
       "      <td>0.678811</td>\n",
       "      <td>0.312064</td>\n",
       "      <td>1.369864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.011772</td>\n",
       "      <td>0.014396</td>\n",
       "      <td>14.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>0.014478</td>\n",
       "      <td>0.985522</td>\n",
       "      <td>0.014690</td>\n",
       "      <td>0.042683</td>\n",
       "      <td>0.060906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006018</td>\n",
       "      <td>0.030942</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.908537</td>\n",
       "      <td>0.464434</td>\n",
       "      <td>0.596472</td>\n",
       "      <td>0.457653</td>\n",
       "      <td>0.602879</td>\n",
       "      <td>0.330288</td>\n",
       "      <td>1.506995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.014871</td>\n",
       "      <td>0.018114</td>\n",
       "      <td>16.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>0.016842</td>\n",
       "      <td>0.983158</td>\n",
       "      <td>0.017131</td>\n",
       "      <td>0.048780</td>\n",
       "      <td>0.059692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007263</td>\n",
       "      <td>0.032779</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.865854</td>\n",
       "      <td>0.524126</td>\n",
       "      <td>0.535566</td>\n",
       "      <td>0.517121</td>\n",
       "      <td>0.542347</td>\n",
       "      <td>0.341199</td>\n",
       "      <td>1.596493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.018115</td>\n",
       "      <td>0.021889</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>2348.0</td>\n",
       "      <td>0.017462</td>\n",
       "      <td>0.982538</td>\n",
       "      <td>0.017772</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.147440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009520</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.307927</td>\n",
       "      <td>0.817073</td>\n",
       "      <td>0.671566</td>\n",
       "      <td>0.475874</td>\n",
       "      <td>0.664100</td>\n",
       "      <td>0.482879</td>\n",
       "      <td>0.363640</td>\n",
       "      <td>1.692085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.022342</td>\n",
       "      <td>0.030438</td>\n",
       "      <td>20.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.060976</td>\n",
       "      <td>0.046654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010652</td>\n",
       "      <td>0.042303</td>\n",
       "      <td>0.368902</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.718221</td>\n",
       "      <td>0.328434</td>\n",
       "      <td>0.711049</td>\n",
       "      <td>0.335900</td>\n",
       "      <td>0.349318</td>\n",
       "      <td>2.060356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.030553</td>\n",
       "      <td>0.037088</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>0.029391</td>\n",
       "      <td>0.970609</td>\n",
       "      <td>0.030281</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.086534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012702</td>\n",
       "      <td>0.044844</td>\n",
       "      <td>0.493902</td>\n",
       "      <td>0.631098</td>\n",
       "      <td>0.804755</td>\n",
       "      <td>0.281779</td>\n",
       "      <td>0.798372</td>\n",
       "      <td>0.288951</td>\n",
       "      <td>0.310852</td>\n",
       "      <td>2.184095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.037455</td>\n",
       "      <td>0.057047</td>\n",
       "      <td>34.0</td>\n",
       "      <td>842.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>0.038813</td>\n",
       "      <td>0.961187</td>\n",
       "      <td>0.040380</td>\n",
       "      <td>0.103659</td>\n",
       "      <td>0.053812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014380</td>\n",
       "      <td>0.051537</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>0.506098</td>\n",
       "      <td>0.858567</td>\n",
       "      <td>0.195245</td>\n",
       "      <td>0.853208</td>\n",
       "      <td>0.201628</td>\n",
       "      <td>0.261006</td>\n",
       "      <td>2.510062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.061511</td>\n",
       "      <td>0.075137</td>\n",
       "      <td>46.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>0.046371</td>\n",
       "      <td>0.953629</td>\n",
       "      <td>0.048626</td>\n",
       "      <td>0.140244</td>\n",
       "      <td>0.060459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016550</td>\n",
       "      <td>0.056290</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>0.919026</td>\n",
       "      <td>0.141433</td>\n",
       "      <td>0.915305</td>\n",
       "      <td>0.146792</td>\n",
       "      <td>0.181221</td>\n",
       "      <td>2.741562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.093339</td>\n",
       "      <td>0.093339</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1267.0</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>0.063562</td>\n",
       "      <td>0.936438</td>\n",
       "      <td>0.067877</td>\n",
       "      <td>0.262195</td>\n",
       "      <td>0.080974</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020532</td>\n",
       "      <td>0.063562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.262195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084695</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.095763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         min       max  bads   goods   total  bad_rate  good_rate      odds  \\\n",
       "0   0.001873  0.003195   2.0   922.0   924.0  0.002165   0.997835  0.002169   \n",
       "1   0.003623  0.004079   1.0  1053.0  1054.0  0.000949   0.999051  0.000950   \n",
       "2   0.004275  0.006260   3.0  1392.0  1395.0  0.002151   0.997849  0.002155   \n",
       "3   0.006621  0.008334   6.0   866.0   872.0  0.006881   0.993119  0.006928   \n",
       "4   0.008360  0.008793  11.0   875.0   886.0  0.012415   0.987585  0.012571   \n",
       "5   0.008883  0.010726   7.0  1206.0  1213.0  0.005771   0.994229  0.005804   \n",
       "6   0.011772  0.014396  14.0   953.0   967.0  0.014478   0.985522  0.014690   \n",
       "7   0.014871  0.018114  16.0   934.0   950.0  0.016842   0.983158  0.017131   \n",
       "8   0.018115  0.021889  41.0  2307.0  2348.0  0.017462   0.982538  0.017772   \n",
       "9   0.022342  0.030438  20.0   730.0   750.0  0.026667   0.973333  0.027397   \n",
       "10  0.030553  0.037088  41.0  1354.0  1395.0  0.029391   0.970609  0.030281   \n",
       "11  0.037455  0.057047  34.0   842.0   876.0  0.038813   0.961187  0.040380   \n",
       "12  0.061511  0.075137  46.0   946.0   992.0  0.046371   0.953629  0.048626   \n",
       "13  0.093339  0.093339  86.0  1267.0  1353.0  0.063562   0.936438  0.067877   \n",
       "\n",
       "    bad_prop  good_prop  ...  cum_bad_rate  cum_bad_rate_rev  cum_bads_prop  \\\n",
       "0   0.006098   0.058925  ...      0.002165          0.020532       0.006098   \n",
       "1   0.003049   0.067297  ...      0.001517          0.021660       0.009146   \n",
       "2   0.009146   0.088963  ...      0.001779          0.023219       0.018293   \n",
       "3   0.018293   0.055346  ...      0.002827          0.025551       0.036585   \n",
       "4   0.033537   0.055921  ...      0.004483          0.026939       0.070122   \n",
       "5   0.021341   0.077075  ...      0.004729          0.028126       0.091463   \n",
       "6   0.042683   0.060906  ...      0.006018          0.030942       0.134146   \n",
       "7   0.048780   0.059692  ...      0.007263          0.032779       0.182927   \n",
       "8   0.125000   0.147440  ...      0.009520          0.034742       0.307927   \n",
       "9   0.060976   0.046654  ...      0.010652          0.042303       0.368902   \n",
       "10  0.125000   0.086534  ...      0.012702          0.044844       0.493902   \n",
       "11  0.103659   0.053812  ...      0.014380          0.051537       0.597561   \n",
       "12  0.140244   0.060459  ...      0.016550          0.056290       0.737805   \n",
       "13  0.262195   0.080974  ...      0.020532          0.063562       1.000000   \n",
       "\n",
       "    cum_bads_prop_rev  cum_goods_prop  cum_goods_prop_rev  cum_total_prop  \\\n",
       "0            1.000000        0.058925            1.000000        0.057840   \n",
       "1            0.993902        0.126222            0.941075        0.123818   \n",
       "2            0.990854        0.215185            0.873778        0.211142   \n",
       "3            0.981707        0.270531            0.784815        0.265728   \n",
       "4            0.963415        0.326452            0.729469        0.321189   \n",
       "5            0.929878        0.403528            0.673548        0.397121   \n",
       "6            0.908537        0.464434            0.596472        0.457653   \n",
       "7            0.865854        0.524126            0.535566        0.517121   \n",
       "8            0.817073        0.671566            0.475874        0.664100   \n",
       "9            0.692073        0.718221            0.328434        0.711049   \n",
       "10           0.631098        0.804755            0.281779        0.798372   \n",
       "11           0.506098        0.858567            0.195245        0.853208   \n",
       "12           0.402439        0.919026            0.141433        0.915305   \n",
       "13           0.262195        1.000000            0.080974        1.000000   \n",
       "\n",
       "    cum_total_prop_rev        ks      lift  \n",
       "0             1.000000  0.052827  1.000000  \n",
       "1             0.942160  0.117076  1.054919  \n",
       "2             0.876182  0.196892  1.130877  \n",
       "3             0.788858  0.233946  1.244467  \n",
       "4             0.734272  0.256330  1.312067  \n",
       "5             0.678811  0.312064  1.369864  \n",
       "6             0.602879  0.330288  1.506995  \n",
       "7             0.542347  0.341199  1.596493  \n",
       "8             0.482879  0.363640  1.692085  \n",
       "9             0.335900  0.349318  2.060356  \n",
       "10            0.288951  0.310852  2.184095  \n",
       "11            0.201628  0.261006  2.510062  \n",
       "12            0.146792  0.181221  2.741562  \n",
       "13            0.084695 -0.000000  3.095763  \n",
       "\n",
       "[14 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toad.metrics.KS_bucket(prob_off,offy,\n",
    "                       bucket=15,\n",
    "                       method='quantile') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-surprise",
   "metadata": {},
   "source": [
    "## 10 生成评分卡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "solved-vintage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[-inf ~ 0.02)</td>\n",
       "      <td>158.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.02 ~ 0.04)</td>\n",
       "      <td>122.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.04 ~ 0.11)</td>\n",
       "      <td>73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.11 ~ inf)</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[-inf ~ 0.16666666666666666)</td>\n",
       "      <td>94.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.16666666666666666 ~ 0.35897435897435903)</td>\n",
       "      <td>117.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.35897435897435903 ~ inf)</td>\n",
       "      <td>125.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-inf ~ -0.2610139784946237)</td>\n",
       "      <td>172.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.2610139784946237 ~ -0.1286774193548387)</td>\n",
       "      <td>143.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.1286774193548387 ~ -0.0537175627240143)</td>\n",
       "      <td>123.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.0537175627240143 ~ 0.013863440860215)</td>\n",
       "      <td>120.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.013863440860215 ~ 0.0626602150537634)</td>\n",
       "      <td>108.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.0626602150537634 ~ 0.078853046594982)</td>\n",
       "      <td>90.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.078853046594982 ~ inf)</td>\n",
       "      <td>75.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                                        value   score\n",
       "0   credit_info                                [-inf ~ 0.02)  158.16\n",
       "1   credit_info                                [0.02 ~ 0.04)  122.58\n",
       "2   credit_info                                [0.04 ~ 0.11)   73.93\n",
       "3   credit_info                                 [0.11 ~ inf)   43.92\n",
       "4      act_info                 [-inf ~ 0.16666666666666666)   94.95\n",
       "5      act_info  [0.16666666666666666 ~ 0.35897435897435903)  117.49\n",
       "6      act_info                  [0.35897435897435903 ~ inf)  125.15\n",
       "7   person_info                 [-inf ~ -0.2610139784946237)  172.88\n",
       "8   person_info  [-0.2610139784946237 ~ -0.1286774193548387)  143.01\n",
       "9   person_info  [-0.1286774193548387 ~ -0.0537175627240143)  123.80\n",
       "10  person_info    [-0.0537175627240143 ~ 0.013863440860215)  120.84\n",
       "11  person_info     [0.013863440860215 ~ 0.0626602150537634)  108.88\n",
       "12  person_info     [0.0626602150537634 ~ 0.078853046594982)   90.34\n",
       "13  person_info                    [0.078853046594982 ~ inf)   75.46"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from toad.scorecard import ScoreCard  \n",
    "card = ScoreCard(combiner=combiner, \n",
    "                    transer=t, C=0.1, \n",
    "                    class_weight='balanced', \n",
    "                    base_score=600,\n",
    "                    base_odds=35,\n",
    "                    pdo=60,\n",
    "                    rate=2)  \n",
    "card.fit(x,y)  \n",
    "final_card = card.export(to_frame=True)  \n",
    "final_card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "infrared-saturn",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[-inf ~ 0.02)</td>\n",
       "      <td>158.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.02 ~ 0.04)</td>\n",
       "      <td>122.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.04 ~ 0.11)</td>\n",
       "      <td>73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.11 ~ inf)</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[-inf ~ 0.16666666666666666)</td>\n",
       "      <td>94.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.16666666666666666 ~ 0.35897435897435903)</td>\n",
       "      <td>117.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.35897435897435903 ~ inf)</td>\n",
       "      <td>125.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-inf ~ -0.2610139784946237)</td>\n",
       "      <td>172.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.2610139784946237 ~ -0.1286774193548387)</td>\n",
       "      <td>143.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.1286774193548387 ~ -0.0537175627240143)</td>\n",
       "      <td>123.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.0537175627240143 ~ 0.013863440860215)</td>\n",
       "      <td>120.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.013863440860215 ~ 0.0626602150537634)</td>\n",
       "      <td>108.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.0626602150537634 ~ 0.078853046594982)</td>\n",
       "      <td>90.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.078853046594982 ~ inf)</td>\n",
       "      <td>75.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                                        value   score\n",
       "0   credit_info                                [-inf ~ 0.02)  158.16\n",
       "1   credit_info                                [0.02 ~ 0.04)  122.58\n",
       "2   credit_info                                [0.04 ~ 0.11)   73.93\n",
       "3   credit_info                                 [0.11 ~ inf)   43.92\n",
       "4      act_info                 [-inf ~ 0.16666666666666666)   94.95\n",
       "5      act_info  [0.16666666666666666 ~ 0.35897435897435903)  117.49\n",
       "6      act_info                  [0.35897435897435903 ~ inf)  125.15\n",
       "7   person_info                 [-inf ~ -0.2610139784946237)  172.88\n",
       "8   person_info  [-0.2610139784946237 ~ -0.1286774193548387)  143.01\n",
       "9   person_info  [-0.1286774193548387 ~ -0.0537175627240143)  123.80\n",
       "10  person_info    [-0.0537175627240143 ~ 0.013863440860215)  120.84\n",
       "11  person_info     [0.013863440860215 ~ 0.0626602150537634)  108.88\n",
       "12  person_info     [0.0626602150537634 ~ 0.078853046594982)   90.34\n",
       "13  person_info                    [0.078853046594982 ~ inf)   75.46"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_card = card.export(to_frame=True)  \n",
    "final_card"
   ]
  }
 ],
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